Open Software Development Research Articles

Evaluation and Acceptance of Open-Source 3D-Printed Tracheobronchial Tree Models for Improving Pediatric Lung Isolation Techniques Training

Evaluation and Acceptance of Open-Source 3D-Printed Tracheobronchial Tree Models for Improving Pediatric Lung Isolation Techniques Training

Co-creating the future: participatory cities and digital governance.

The digital revolution, fuelled by advancements in social media, Big Data, the Internet of Things and Artificial Intelligence, is reshaping our urban landscapes into 'participatory cities'. These cities leverage digital technologies to foster citizen engagement, collaborative decision-making and community-driven urban development, thus unlocking new potentials while confronting emerging threats. Such technologies are empowering individuals and organizations in ways that were unimaginable just a few years ago. They do, however, introduce new risks and vulnerabilities that must be carefully managed. Hence, socio-technical innovation is urgently needed. In this connection, open-source technologies, participatory approaches and new forms of governance are becoming more popular and relevant. This theme issue looks into the tangible impacts of these technological advancements, with a focus on participatory cities. It aims to explain how digital tools are used in cities to tackle urban challenges, improve governance and promote sustainability. Through a collection of in-depth analyses, case studies and real-world examples, this issue seeks to offer a comprehensive understanding of the digital governance frameworks underpinning participatory cities. By offering a platform for multidisciplinary discourse, this theme issue endeavours to contribute to the broader narrative of shaping a more resilient, sustainable and democratic urban future in the digital age.This article is part of the theme issue 'Co-creating the future: participatory cities and digital governance'.

Empowering Youth through Open Data: Map-Based Storytelling of Croatian vs. European Youth

Abstract. In the digital age, open data has become a critical asset for fostering transparency, accountability, and civic engagement. However, the widespread availability of data has outpaced citizens' ability to effectively utilize it, underscoring the need for data literacy skills, especially among youth. This paper addresses the gap in data literacy by proposing an innovative teaching approach that leverages map-based storytelling to engage young people and enhance their data literacy. By integrating real-world open datasets into interactive visual narratives, this approach aims to captivate young audiences, develop critical thinking skills, and foster a deeper understanding of socio-economic issues. Through a case study of a Datathon project titled “Croatian Youth vs. EU: Who has it better?”, this research demonstrates the efficacy of combining data visualization and storytelling to promote data literacy and civic engagement. The findings highlight the potential of open data initiatives and open-source technologies in empowering youth and enhancing their participation in open government initiatives and policy-making processes.

Abstract 4138647: Opportunistic Screening for Cardiovascular Risk Using Chest X-Rays and Deep Learning: Associations with Coronary Artery Disease in the Project Baseline Health Study and Mass General Brigham Biobank

Introduction/Background: We previously demonstrated that an open-source deep learning model (CXR-CVD Risk) can predict 10-year major adverse cardiovascular events (myocardial infarction&stroke), based on a chest radiograph image (CXR). As deep learning models are black boxes, establishing the biological processes the model captures to predict risk may help build understanding and trust in the model. Research Questions/Hypothesis: To test associations between deep-learning derived CXR-CVD Risk and markers of cardiovascular disease including coronary artery calcium (CAC) and stenosis ≥50% on CT, systolic blood pressure (SBP), ankle brachial index (ABI), and prevalent myocardial infarction and stroke. Methods/Approach: We conducted external validation of CXR-CVD-Risk in two cohorts: 1) 2,097 volunteers in the Project Baseline Health Study (PBHS) and 2) 1,644 Mass General Brigham Biobank (MGBB) patients. The CXR-CVD-Risk model estimated 10-year cardiovascular event risk (probability between 0 and 1) from a CXR image. We calculated linear associations with SBP, ABI, and the logarithm of coronary artery calcium and odds ratios for prevalent hypertension, myocardial infarction, stroke, and, in the MGBB, coronary artery stenosis ≥50%. Analyses were adjusted for age, BMI, sex, smoking status, and enrolling site. Results/Data: CXR-CVD-Risk was associated with CAC in both populations (PBHS: 1.11-fold increase, 95% CI: [1.07-1.16]; MGBB: 1.03-fold increase [1.01-1.05] in CAC per 1% increase in CXR-CV-Risk). CXR-CVD-Risk was also associated with SBP (0.59 mmHg increase [0.24-0.93] in SBP per 1% increase in CXR-CV-Risk), history of hypertension, history of myocardial infarction, and stroke. There was an inverse association with ABI (0.010 decrease [0.005-0.014] in ABI) in the PBHS. In the MGBB, CXR-CVD-Risk was associated with coronary artery stenosis ≥50% (OR = 1.004 [1.002-1.007]). All estimates are after covariate adjustment. Conclusion: This deep learning CXR risk score was associated with coronary artery disease (calcium score and stenosis ≥50%), CVD risk factors, and prevalent CVD. Opportunistic screening using CXRs in the electronic record can identify patients at high risk of CVD who may benefit from prevention.

Abstract 4119426: Enhancing early detection of ICI myocarditis cases during hospitalization: A role for large language models

Background: Immune-checkpoint inhibitor (ICI)-induced myocarditis is the most fatal immune-related adverse event (irAE). Early recognition and treatment of ICI myocarditis is associated with improved outcomes. However, current approaches to the diagnosis of ICI myocarditis have limitations. Open-source large language models (LLMs) are an accessible and scalable method of answering queries from human-generated text, and therefore may assist in the real-time early detection of ICI myocarditis among at-risk patients. Research Question: Can a free, open-source LLM detect cases of ICI myocarditis early in admission? Aims: We investigated the ability of a LLM pipeline to screen for ICI myocarditis within one day of admission using hospital medical records. Methods: Hospital admissions of patients on ICI therapy from November 4th, 2021, to September 5th, 2023 were retrospectively reviewed by a multidisciplinary immunotoxicity team using established, published definitions for the presence of ICI myocarditis. Progress notes written the day before, day of, and day after admission were fed into an open-source LLM pipeline built using Mistral 7B OpenOrca with retrieval augmentation generation (RAG). Performance of the LLM was measured via sensitivity and specificity in comparison to the gold standard of manual adjudication. Results: Of the 1874 hospital admissions of patients on ICI therapy, there were 22 (1.2%) cases of myocarditis. The average time to initiation of treatment was 2.45 days. Using notes written within one day after admission, the LLM detected ICI myocarditis with 95.5±8.7% sensitivity and 95.4±1.0% specificity, spending 2.13 seconds per chart. Conclusion: LLMs serve as a useful tool to screen for ICI myocarditis, detecting 95.5% of cases within one day of admission with 95.4% specificity. Future studies will investigate if integrating this tool into the real-time management of irAEs could lead to earlier diagnosis, faster initiation of treatment, and improved patient outcomes.

Exploring the Application of Large Language Models Based AI Agents in Leakage Detection of Natural Gas Valve Chambers

Leakage problems occur from time to time due to the large number of equipment and complex processes during oil and gas production and transportation. The traditional detection methods highly depend on manpower with large workload and are prone to missed and false alarms, which seriously affects the efficiency and safety of oil and gas production and transportation. With the continuous improvement of information technology and the rapid advancement of artificial intelligence (AI), the research on leakage detection technology based on AI methods has attracted more and more attention. This paper discusses the application scenarios of an AI agent based on the recently emerged large language model (LLM) technology in oil and gas production leakage detection: (1) Compared with the traditional leakage detection methods, this paper innovatively employs a combination of AI-based diagnostics and infrared temperature measurement technologies to develop a specialized small model for oil and gas leakage detection, which has been proven to significantly improve the accuracy of detecting industrial venting events in natural gas valve chambers; (2) By employing retrieval-augmented generation (RAG) technology, a knowledge vector library is constructed, utilizing a series of leakage-related documents, assisting the LLM to carry out knowledge questioning and inference. Compared with the traditional SimBERT, the accuracy can be improved by about 15% in the Q&A search ability test. The correct rate is about 70% higher than the SimBERT in the Chinese complex reasoning quiz. Also, it can still remain stable under high load conditions, with the interruption rate of retrieval closing to zero. (3) By combining the specialized small model and the knowledge Q&A tool, the natural gas valve chambers’ leakage detection AI agent based on the open-source LLM model was designed and developed, which preliminarily achieved the leakage detection based on the specialized small model, and the automatic processing of the retrieval reasoning process based on the knowledge Q&A tool and the intelligent generation of corresponding leakage disposal scheme. The effectiveness of the method has been verified by actual project data. This article conducts preliminary explorations into the in-depth applications of AI agents based on LLMs in the oil and gas energy industry, demonstrating certain positive outcomes.

Custom Large Language Models Improve Accuracy: Comparing Retrieval Augmented Generation and Artificial Intelligence Agents to Non-Custom Models for Evidence-Based Medicine

The purpose of the study is to demonstrate the value of custom methods, namely Retrieval Augmented Generation(RAG)-based Large Language Models(LLMs) and Agentic Augmentation, over standard LLMs in delivering accurate information using an anterior cruciate ligament(ACL) injury case. A set of 100 questions and answers based on the 2022 AAOS ACL guidelines were curated. Closed-source(Open AI GPT4/GPT 3.5 and Anthropic's Claude3) and open-source models(LLama3 8b/70b and Mistral8x7b) were asked questions in base form and again with AAOS guidelines embedded into a RAG system. The top-performing models were further augmented with Artificial Intelligence(AI) Agents and re-evaluated. Two fellowship-trained surgeons blindly evaluated the accuracy of the responses of each cohort. ROUGE and METEOR scores were calculated to assess semantic similarity in the response. All non-custom LLM models started below 60% accuracy. Applying RAG improved the accuracy of every model by an average 39.7%. The highest performing model with just RAG was Meta's Open-Source Llama3 70b(94%). The highest performing model with RAG and AI Agents was Open AI's GPT4(95%). RAG improved accuracy by an average of 39.7%, with the highest accuracy rate of 94% in the Meta Llama3 70b. Incorporating AI agents into a previously RAG-augmented LLM improved ChatGPT4 accuracy rate to 95%. Thus, Agentic and RAG augmented LLMs can be accurate liaisons of information, supporting our hypothesis. Despite literature surrounding the use of LLM in medicine, there has been considerable and appropriate skepticism given the variably accurate response rates. This study establishes the groundwork to identify whether custom modifications to LLMs using RAG and Agentic augmentation can better deliver accurate information in orthopaedic care. With this knowledge, online medical information commonly sought in popular LLMs, such as ChatGPT, can be standardized and provide relevant online medical information to better support shared decision making between surgeon and patient.

"A Lot of Moving Parts": A Case Study of Open-Source Hardware Design Collaboration in the Thingiverse Community

Open-source is a decentralized and collaborative method of development that encourages open contribution from an extensive and undefined network of individuals. Although commonly associated with software development (OSS), the open-source model extends to hardware development, forming the basis of open-source hardware development (OSH). Compared to OSS, OSH is relatively nascent, lacking adequate tooling support from existing platforms and best practices for efficient collaboration. Taking a necessary step towards improving OSH collaboration, we conduct a detailed case study of DrawBot, a successful OSH project that remarkably fostered a long-term collaboration on Thingiverse - a platform not explicitly intended for complex collaborative design. Through analyzing comment threads and design changes over the course of the project, we found how collaboration occurred, the challenges faced, and how the DrawBot community managed to overcome these obstacles. Beyond offering a detailed account of collaboration practices and challenges, our work contributes best practices, design implications, and practical implications for OSH project maintainers, platform builders, and researchers, respectively. With these insights and our publicly available dataset, we aim to foster more effective and efficient collaborative design in OSH projects.

Diagnostics-LLaVA

The recent advancements in the area of Large language models (LLMs) has opened horizons for conversational assistant-based intelligent models capable of interpreting images, and providing textual response, also known as Visual language models (VLMs). These models can assist equipment operators and maintenance technicians in the complex Prognostics and Health Management (PHM) tasks such as diagnostics of faults, root cause analysis and repair recommendation. Significant open source contributions in the area of VLMs have been made. However, models trained on general data fail to perform well in complex tasks in specialized domains such as diagnostics and repair of industrial equipment. Therefore, in this paper we discuss our work on development of Diagnostics-LLaVA, a VLM suitable for interpreting images of specific industrial equipment and provide better response than existing open source models in PHM tasks such as fault diagnostics and repair recommendation. We introduce Diagnostics-LLaVA based on the architecture of LLaVA, and created one instance of the Diagnostics-LLaVA for the automotive repair domain, referred to as Automotive-LLaVA. We demonstrate that our proposed Automotive-LLaVA model performs better than the state-of-the-art open source visual language models such as mPlugOWL and LLaVA in both qualitative and quantitative experiments.

The PRIDE database at 20 years: 2025 update.

The PRoteomics IDEntifications (PRIDE) database (https://www.ebi.ac.uk/pride/) is the world's leading mass spectrometry (MS)-based proteomics data repository and one of the founding members of the ProteomeXchange consortium. This manuscript summarizes the developments in PRIDE resources and related tools for the last three years. The number of submitted datasets to PRIDE Archive (the archival component of PRIDE) has reached on average around 534 datasets per month. This has been possible thanks to continuous improvements in infrastructure such as a new file transfer protocol for very large datasets (Globus), a new data resubmission pipeline and an automatic dataset validation process. Additionally, we will highlight novel activities such as the availability of the PRIDE chatbot (based on the use of open-source Large Language Models), and our work to improve support for MS crosslinking datasets. Furthermore, we will describe how we have increased our efforts to reuse, reanalyze and disseminate high-quality proteomics data into added-value resources such as UniProt, Ensembl and Expression Atlas.

Raising awareness of potential biases in medical machine learning: Experience from a Datathon.

To challenge clinicians and informaticians to learn about potential sources of bias in medical machine learning models through investigation of data and predictions from an open-source severity of illness score. Over a two-day period (total elapsed time approximately 28 hours), we conducted a datathon that challenged interdisciplinary teams to investigate potential sources of bias in the Global Open Source Severity of Illness Score. Teams were invited to develop hypotheses, to use tools of their choosing to identify potential sources of bias, and to provide a final report. Five teams participated, three of which included both informaticians and clinicians. Most (4/5) used Python for analyses, the remaining team used R. Common analysis themes included relationship of the GOSSIS-1 prediction score with demographics and care related variables; relationships between demographics and outcomes; calibration and factors related to the context of care; and the impact of missingness. Representativeness of the population, differences in calibration and model performance among groups, and differences in performance across hospital settings were identified as possible sources of bias. Datathons are a promising approach for challenging developers and users to explore questions relating to unrecognized biases in medical machine learning algorithms. Disadvantaged groups are at risk of being adversely impacted by biased medical machine learning models. To avoid these undesirable outcomes, developers and users must understand the challenges involved in identifying potential biases. We conducted a datathon aimed at challenging a diverse group of participants to explore an open-source patient severity model for potential biases. Five groups of clinicians and informaticians used tools of their choosing to evaluate possible sources of biases, applying a range of analytic techniques and exploring multiple features. By engaging diverse participants with hands-on data experience with meaningful data, datathons have the potential to raise awareness of potential biases and promote best practices in developing fair and equitable medical machine learning models.

A novel method for tracing gasoline using GC-IRMS and Relief-Stacking fusion model

Due to China’s vast geographical expanse and the complexity of its natural environment, traditional methods are not suitable for tracing the origins of gasoline samples from different regions of China. The aim of this study is to trace the origins of gasoline samples through the analysis of carbon stable isotopic characteristics from 60 gasoline samples collected across four major regions in China. In this study, a novel method that combines gas chromatography-isotope ratio mass spectrometry (GC-IRMS) with Relief-Stacking was proposed for the first time, significantly enhancing the precision of the provenance results. Firstly, eight characteristic components were identified by Relief feature selection model for the first time and applied to the assessment of carbon stable isotope ratios across different regions. The discovery of these components effectively reduces data redundancy, thereby enhancing the accuracy of provenance analysis. Subsequently, five effective source tracing models (XGBoost, Random Forest, CatBoost, LightGBM, and KNN) were selected based on the CEI values. To further enhance the learning efficiency of the sourcing models, this study integrated the five selected source tracing models using three different methods(voting, stacking, and blending) and validated the performance of the optimal sourcing model. The results indicated that the Stacking fusion model achieves a CEI value of 0.923, significantly outperforming other fusion models. This advantage is attributed to the Stacking fusion model combining the s trengths of Boosting and Bagging ensemble methods and leveraging the advantages of individual classifiers within the model. The method proposed in this study can effectively address the limitations of previous tracing methods and offer valuable insights for the development of platforms related to stable isotope source tracing. In future research, through appropriate parameter adjustments and feature adaptations, the Relief-Stacking model can likewise be applied to source tracing studies in other countries characterized by extensive geographic ranges and variable environmental conditions.

Unlocking new export opportunities: An open-source framework for assessing green iron and steel supply chains

Globally traded commodities such as iron and steel are coming under international scrutiny owing to their significant embedded greenhouse emissions. Commodity export-oriented nations must explore opportunities for decarbonising these supply chains or risk losing market share as customers seek cleaner alternatives and as initiatives such as carbon border adjustment mechanisms (CBAM) come into effect. Here we present an open-source model developed to assist industry and other stakeholders in assessing the costs of potential CO2 abatement pathways in supplying green iron ore, green iron, or green steel to key global markets. Using a case study, we assess the development of a green steel supply chain from major iron ore producers of Australia, Brazil, and Sweden, to key markets in Europe, China, and India. Projected costs of renewable energy and other key technologies are used to estimate possible future costs of different decarbonisation pathways. Our modelling suggests that by 2050, export costs of green iron ore, iron and steel could range from 48 to 150, 370–600 and 540–810 USD/tonne respectively. Access to low-cost renewable energy is the key driver of cost reductions for value added products such as iron and steel, and given their substantial renewable energy potential, in 2050 Australia and Brazil could outcompete Sweden in providing these products, with both even outcompeting Sweden in exporting to the European market. The release of our model as open source allows any and all stakeholders to explore the implications of key technology and market uncertainties over least cost green iron and steel supply chains.

Comparing the dental knowledge of large language models.

Introduction With the advancement of artificial intelligence, large language models (LLMs) have emerged as technology that can generate human-like text across various domains. They hold vast potential in the dental field, able to be integrated into clinical dentistry, administrative dentistry, and for student and patient education. However, the successful integration of LLMs into dentistry is reliant on the dental knowledge of the models used, as inaccuracies can lead to significant risks in patient care and education.Aims We are the first to compare different LLMs on their dental knowledge through testing the accuracy of different model responses to Integrated National Board Dental Examination (INBDE) questions.Methods We include closed-source and open-source models and analysed responses to both 'patient box' style board questions and more traditional, textual-based, multiple-choice questions.Results For the entire INBDE question bank, ChatGPT-4 had the highest dental knowledge, with an accuracy of 75.88%, followed by Claude-2.1 with 66.38% and then Mistral-Medium at 54.77%. There was a statistically significant difference in performance across all models.Conclusion Our results highlight the high potential of LLM integration into the dental field, the importance of which LLM is chosen when developing new technologies, and the limitations that must be overcome before unsupervised clinical integration can be adopted.

OpenCCM: An Open-Source Continuous Compartmental Modelling Package

OpenCCM: An Open-Source Continuous Compartmental Modelling Package

Adversarial robustness improvement for X-ray bone segmentation using synthetic data created from computed tomography scans

Deep learning-based image analysis offers great potential in clinical practice. However, it faces mainly two challenges: scarcity of large-scale annotated clinical data for training and susceptibility to adversarial data in inference. As an example, an artificial intelligence (AI) system could check patient positioning, by segmenting and evaluating relative positions of anatomical structures in medical images. Nevertheless, data to train such AI system might be highly imbalanced with mostly well-positioned images being available. Thus, we propose the use of synthetic X-ray images and annotation masks forward projected from 3D photon-counting CT volumes to create realistic non-optimally positioned X-ray images for training. An open-source model (TotalSegmentator) was used to annotate the clavicles in 3D CT volumes. We evaluated model robustness with respect to the internal (simulated) patient rotation α\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha$$\\end{document} on real-data-trained models and real&synthetic-data-trained models. Our results showed that real&synthetic- data-trained models have Dice score percentage improvements of 3% to 15% across different α\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha$$\\end{document} groups compared to the real-data-trained model. Therefore, we demonstrated that synthetic data could be supplementary used to train and enrich heavily underrepresented conditions to increase model robustness.

Left atrial appendage volume estimated by artificial intelligence predicts atrial fibrillation recurrence after cryobaloon ablation

Abstract Background/Introduction The volume of the left atrial appendage (LAA), as measured in contrast-enhanced cardiac computed tomography (CCTA), has been previously reported as a predictor of long-term recurrence of atrial fibrillation (AF) following cryoballoon ablation procedures. Unfortunately manual assessment of LAA volume, is tedious and time-consuming, making it impractical for clinical practice. The recent advent of artificial intelligence (AI)-based methods offers a potential solution to this limitation. Purpose This study aims to evaluate the utility of LAA volume, as automatically measured by a state-of-the-art AI model for the fully automatic segmentation of LAA in CCTA, in predicting AF recurrence following cryoballoon ablation procedures. Methods We retrospectively analyzed a cohort of patients who underwent cryoballoon ablation for pulmonary vein isolation at our facility between 2010 and 2016. AF recurrence was determined through in-person visits and Holter monitoring at 3, 6, and 12 months post-ablation, followed by annual visits. The first three months post-ablation were considered a blanking period. An open-source deep neural network AI model [1] was utilized to perform automatic segmentation and calculation of LAA and left atrium (LA) volumes in. Receiver-operating characteristic curves were used to obtain optimal cut-off values for increased LAA and LA volumes. We then assessed the association of increased LAA and LA volume, and the LAA to LA volume ratio with recurrence-free survival using the Cox regression model. Results The cohort consisted of 212 patients (median age 62, inter-quartile range [IQR]: 55–65), with 25% having persistent AF before the procedure. Subjects were observed for a median period of 12 months (IQR: 12–36), with 70 of them (33%) experiencing a documented recurrent AF episode. The optimal thresholds for predicting AF recurrence were established as 9.8ml for LAA volume, 124ml for LA volume, and 0.086 for the LAA to LA volume ratio, respectively. An increased LAA volume demonstrated the strongest association with AF recurrence (hazard ratio [HR]: 2.6, 95% confidence interval [CI]: 1.55–4.2), followed by an increased LA volume (HR: 2.1, 95% CI: 1.3–3.4), persistence of AF (HR: 2, 95% CI: 1.2–3.3), and increased LAA to LA volume ratio (HR: 1.9, 95% CI: 1.1–3). Kaplan-Meier plots illustrating AF-free survival, are presented in Figure 1. After adjusting for age, persistent AF, left ventricular ejection fraction, and body surface area, all AI measurements remained significantly associated with a higher AF recurrence (Table 1). Conclusions Increased LAA volume, as assessed automatically by AI in CCTA, is a useful predictor of AF recurrence after catheter ablation for atrial fibrillation. The use of freely available, AI-assisted analysis of CCTA could be instrumental in assessing LAA volume, among other predictors, for long-term ablation success.Figure 1.

CIPHER: Cybersecurity Intelligent Penetration-Testing Helper for Ethical Researcher.

Penetration testing, a critical component of cybersecurity, typically requires extensive time and effort to find vulnerabilities. Beginners in this field often benefit from collaborative approaches with the community or experts. To address this, we develop Cybersecurity Intelligent Penetration-testing Helper for Ethical Researchers (CIPHER), a large language model specifically trained to assist in penetration testing tasks as a chatbot. Unlike software development, penetration testing involves domain-specific knowledge that is not widely documented or easily accessible, necessitating a specialized training approach for AI language models. CIPHER was trained using over 300 high-quality write-ups of vulnerable machines, hacking techniques, and documentation of open-source penetration testing tools augmented in an expert response structure. Additionally, we introduced the Findings, Action, Reasoning, and Results (FARR) Flow augmentation, a novel method to augment penetration testing write-ups to establish a fully automated pentesting simulation benchmark tailored for large language models. This approach fills a significant gap in traditional cybersecurity Q&A benchmarks and provides a realistic and rigorous standard for evaluating LLM's technical knowledge, reasoning capabilities, and practical utility in dynamic penetration testing scenarios. In our assessments, CIPHER achieved the best overall performance in providing accurate suggestion responses compared to other open-source penetration testing models of similar size and even larger state-of-the-art models like Llama 3 70B and Qwen1.5 72B Chat, particularly on insane difficulty machine setups. This demonstrates that the current capabilities of general large language models (LLMs) are insufficient for effectively guiding users through the penetration testing process. We also discuss the potential for improvement through scaling and the development of better benchmarks using FARR Flow augmentation results.

Modelling the Sea Surface of Cardigan Bay

The aim of this project was to model the sea surface of Cardigan Bay, to be able to expand upon QinetiQ’s current wave models, used within their radar assurance activities. The sea surface can cause unwanted detections, which is referred to as clutter. The sea is not the only source of clutter such as litter and birds, have an impact on the returns detected by the radar. This project explored two different approaches, modelling the propagation of waves and modelling the distribution of energy across the sea surface. The first approach explored two open source models, WAVEWATCH III and SWAN, and after having technical errors, two real world, existing examples of where these models were implemented were researched. This research showed a lot of promise, and would be worth expanding upon. The second approach looked into two recognised equations for calculating the energy density of the waves, and then calculating the significant wave height. Overall, modelling the propagation of waves would produce more representative results, particularly SWAN which was a model designed for coastal waters, however to set up the wave models fell outside of the scope of this project.

An open-source approach for measuring corporate impacts on ecosystem services and biodiversity

Existing approaches to evaluating companies on sustainability-related issues include limited accounting of impacts on nature and its contributions to human well-being. Here we present an approach for quantifying the direct impacts of companies’ physical assets on nature based on global maps for eight ecosystem service and biodiversity metrics. We apply this approach to a set of over 2000 global, publicly traded companies with 580,000 mapped physical assets and find that companies in utility, real estate, materials, and financial sectors have the largest impacts on average, with substantial variation within all sectors. Using high-spatial-resolution satellite imagery to map individual mine footprints, we compare a set of active lithium mines and find that impacts vary substantially among mines and change over time. By using open-source models and drawing on the growing availability of high-spatial-resolution satellite imagery, this approach could provide more transparent measures of corporate impacts to nature for nature-related reporting.