Safety Monitoring for Large Language Models: A Case Study of Offshore Wind Maintenance

It has been forecasted that a quarter of the world's energy usage will be supplied from Offshore Wind (OSW) by 2050 (Smith 2023). Given that up to one third of Levelised Cost of Energy (LCOE) arises from Operations and Maintenance (O&M), the motive for cost reduction is enormous. In typical OSW farms hundreds of alarms occur within a single day, making manual O&M planning without automated systems costly and difficult. Increased pressure to ensure safety and high reliability in progressively harsher environments motivates the exploration of Artificial Intelligence (AI) and Machine Learning (ML) systems as aids to the task. We recently introduced a specialised conversational agent trained to interpret alarm sequences from Supervisory Control and Data Acquisition (SCADA) and recommend comprehensible repair actions (Walker et al. 2023). Building on recent advancements on Large Language Models (LLMs), we expand on this earlier work, fine tuning LLAMA (Touvron 2018), using available maintenance records from EDF Energy. An issue presented by LLMs is the risk of responses containing unsafe actions, or irrelevant hallucinated procedures. This paper proposes a novel framework for safety monitoring of OSW, combining previous work with additional safety layers. Generated responses of this agent are being filtered to prevent raw responses endangering personnel and the environment. The algorithm represents such responses in embedding space to quantify dissimilarity to pre-defined unsafe concepts using the Empirical Cumulative Distribution Function (ECDF). A second layer identifies hallucination in responses by exploiting probability distributions to analyse against stochastically generated sentences. Combining these layers, the approach finetunes individual safety thresholds based on categorised concepts, providing a unique safety filter. The proposed framework has potential to utilise the O&M planning for OSW farms using state-of-the-art LLMs as well as equipping them with safety monitoring that can increase technology acceptance within the industry

A Deep Learning Framework for Wind Turbine Repair Action Prediction Using Alarm Sequences and Long Short Term Memory Algorithms

With an increasing emphasis on driving down the costs of Operations and Maintenance (O$\&$M) in the Offshore Wind (OSW) sector, comes the requirement to explore new methodology and applications of Deep Learning (DL) to the domain. Condition-based monitoring (CBM) has been at the forefront of recent research developing alarm-based systems and data-driven decision making. This paper provides a brief insight into the research being conducted in this area, with a specific focus on alarm sequence modelling and the associated challenges faced in its implementation. The paper proposes a novel idea to predict a set of relevant repair actions from an input sequence of alarm sequences, comparing Long Short-term Memory (LSTM) and Bidirectional LSTM (biLSTM) models. Achieving training accuracy results of up to 80.23$\%$, and test accuracy results of up to 76.01$\%$ with biLSTM gives a strong indication to the potential benefits of the proposed approach that can be furthered in future research. The paper introduces a framework that integrates the proposed approach into O$\&$M procedures and discusses the potential benefits which include the reduction of a confusing plethora of alarms, as well as unnecessary vessel transfers to the turbines for fault diagnosis and correction

A massive quiescent galaxy at redshift 4.658

A. C. Carnall thanks the Leverhulme Trust for their support via a Leverhulme Early Career Fellowship. R. J. McLure, J. S. Dunlop, D. J. McLeod, V. Wild, R. Begley, C. T. Donnan and M. L. Hamadouche acknowledge the support of the Science and Technology Facilities Council. F. Cullen acknowledges support from a UKRI Frontier Research Guarantee Grant (grant reference EP/X021025/1). A. Cimatti acknowledges support from the grant PRIN MIUR 2017 - 20173ML3WW 001.The extremely rapid assembly of the earliest galaxies during the first billion years of cosmic history is a major challenge for our understanding of galaxy formation physics (1; 2; 3; 4; 5). The advent of JWST has exacerbated this issue by confirming the existence of galaxies in significant numbers as early as the first few hundred million years (6; 7; 8). Perhaps even more surprisingly, in some galaxies, this initial highly efficient star formation rapidly shuts down, or quenches, giving rise to massive quiescent galaxies as little as 1.5 billion years after the Big Bang (9; 10), however, due to their faintness and red colour, it has proven extremely challenging to learn about these extreme quiescent galaxies, or to confirm whether any exist at earlier times. Here we report the spectroscopic confirmation of a massive quiescent galaxy, GS-9209, at redshift, z = 4.658, just 1.25 billion years after the Big Bang, using JWST NIRSpec. From these data we infer a stellar mass of M∗ = 3.8 ± 0.2 × 1010 M⊙, which formed over a ≃ 200 Myr period before this galaxy quenched its star formation activity at z=6.5+0.2−0.5, when the Universe was ≃ 800 million years old. This galaxy is both a likely descendent of the highest-redshift submillimetre galaxies and quasars, and a likely progenitor for the dense, ancient cores of the most massive local galaxies.PostprintPeer reviewe

How effective are incident reporting systems for improving patient safety? A systematic literature review

Context: Incident reporting systems (IRSs) are used to gather information on patient safety incidents. However, and despite the financial burden they imply, little is known about their effectiveness. This paper reviews systematically the effectiveness of IRSs as a method of improving patient safety through organizational learning. Method: This systematic literature review identified two groups of studies: a) studies comparing the effectiveness of IRSs relative to other methods of error reporting and b) studies examining the effectiveness of IRSs on settings, structures and outcomes in respect of improvements to patient safety. We used thematic analysis to compare the effectiveness of IRSs with other methods and to synthesize what was effective, where and why. Then, to assess the evidence concerning the ability of IRSs to facilitate organizational learning, we analyzed studies using the concepts of single loop and double loop learning. Findings: In total, 43 studies were identified. Eight studies compared IRSs with other methods, while 35 explored the effectiveness of IRSs on settings, structures and outcomes. We did not find strong evidence that IRSs perform better than other methods. We found some evidence of single loop learning, that is, changes to clinical settings or processes as a consequence of learning from IRSs, but little evidence either of improvements to outcomes or of changes to latent managerial factors involved in error production. In addition, there was insubstantial evidence of IRSs enabling double loop learning that is, cultural change or change of mindset. Conclusions: The results indicate IRSs could be more effective if there were explicit criteria for what counts as an incident; they are owned and led by clinical teams rather than centralized hospital departments; and embedded within organizations as part of wider safety programs

A cognitive perspective on equivalent effect: using eye tracking to measure equivalence in source text and target text cognitive effects on readers

Eye-tracking methods have long been used to explore cognitive processing in reading, but the recent burgeoning of such methods in the field of translation studies has focused almost entirely on the translation process or audiovisual translation, neglecting the effects of the translation product itself. This paper presents a proof-of-concept study using eye tracking to compare fixation data between native readers of a French literary source text and native readers of its English translation at specific, corresponding points in the texts. The preliminary data are consistent with previous findings on the relationship between the features of the fixated word and fixation durations. These findings are also consistent with stylistic analyses and indicate that this method can be used to compare the levels of cognitive effort between two readership groups in order to investigate whether their experience is similar – whether an ‘equivalent effect’ has been achieved – thus contributing to the ongoing discourse surrounding equivalence in translation studies

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements

Effect of Hydrocortisone on Mortality and Organ Support in Patients With Severe COVID-19: The REMAP-CAP COVID-19 Corticosteroid Domain Randomized Clinical Trial.

Importance: Evidence regarding corticosteroid use for severe coronavirus disease 2019 (COVID-19) is limited. Objective: To determine whether hydrocortisone improves outcome for patients with severe COVID-19. Design, Setting, and Participants: An ongoing adaptive platform trial testing multiple interventions within multiple therapeutic domains, for example, antiviral agents, corticosteroids, or immunoglobulin. Between March 9 and June 17, 2020, 614 adult patients with suspected or confirmed COVID-19 were enrolled and randomized within at least 1 domain following admission to an intensive care unit (ICU) for respiratory or cardiovascular organ support at 121 sites in 8 countries. Of these, 403 were randomized to open-label interventions within the corticosteroid domain. The domain was halted after results from another trial were released. Follow-up ended August 12, 2020. Interventions: The corticosteroid domain randomized participants to a fixed 7-day course of intravenous hydrocortisone (50 mg or 100 mg every 6 hours) (n = 143), a shock-dependent course (50 mg every 6 hours when shock was clinically evident) (n = 152), or no hydrocortisone (n = 108). Main Outcomes and Measures: The primary end point was organ support-free days (days alive and free of ICU-based respiratory or cardiovascular support) within 21 days, where patients who died were assigned -1 day. The primary analysis was a bayesian cumulative logistic model that included all patients enrolled with severe COVID-19, adjusting for age, sex, site, region, time, assignment to interventions within other domains, and domain and intervention eligibility. Superiority was defined as the posterior probability of an odds ratio greater than 1 (threshold for trial conclusion of superiority >99%). Results: After excluding 19 participants who withdrew consent, there were 384 patients (mean age, 60 years; 29% female) randomized to the fixed-dose (n = 137), shock-dependent (n = 146), and no (n = 101) hydrocortisone groups; 379 (99%) completed the study and were included in the analysis. The mean age for the 3 groups ranged between 59.5 and 60.4 years; most patients were male (range, 70.6%-71.5%); mean body mass index ranged between 29.7 and 30.9; and patients receiving mechanical ventilation ranged between 50.0% and 63.5%. For the fixed-dose, shock-dependent, and no hydrocortisone groups, respectively, the median organ support-free days were 0 (IQR, -1 to 15), 0 (IQR, -1 to 13), and 0 (-1 to 11) days (composed of 30%, 26%, and 33% mortality rates and 11.5, 9.5, and 6 median organ support-free days among survivors). The median adjusted odds ratio and bayesian probability of superiority were 1.43 (95% credible interval, 0.91-2.27) and 93% for fixed-dose hydrocortisone, respectively, and were 1.22 (95% credible interval, 0.76-1.94) and 80% for shock-dependent hydrocortisone compared with no hydrocortisone. Serious adverse events were reported in 4 (3%), 5 (3%), and 1 (1%) patients in the fixed-dose, shock-dependent, and no hydrocortisone groups, respectively. Conclusions and Relevance: Among patients with severe COVID-19, treatment with a 7-day fixed-dose course of hydrocortisone or shock-dependent dosing of hydrocortisone, compared with no hydrocortisone, resulted in 93% and 80% probabilities of superiority with regard to the odds of improvement in organ support-free days within 21 days. However, the trial was stopped early and no treatment strategy met prespecified criteria for statistical superiority, precluding definitive conclusions. Trial Registration: ClinicalTrials.gov Identifier: NCT02735707

Evidence gaps and biodiversity threats facing the marine environment of the United Kingdom’s Overseas Territories

Understanding the evidence base and identifying threats to the marine environment is critical to ensure cost-effective management and to identify priorities for future research. The United Kingdom (UK) government is responsible for approximately 2% of the world’s oceans, most of which belongs to its 14 Overseas Territories (UKOTs). Containing biodiversity of global significance, and far in excess of the UK mainland’s domestic species, there has recently been a strong desire from many of the UKOTs, the UK Government, and NGOs to improve marine management in these places. Implementing evidence-based marine policy is, however, challenged by the disparate nature of scientific research in the UKOTs and knowledge gaps about the threats they face. Here, we address these issues by systematically searching for scientific literature which has examined UKOT marine biodiversity and by exploring publicly available spatial threat data. We find that UKOT marine biodiversity has received consistent, but largely low, levels of scientific interest, and there is considerable geographical and subject bias in research effort. Of particular concern is the lack of research focus on management or threats to biodiversity. The extent and intensity of threats vary amongst and within the UKOTs but unsurprisingly, climate change associated threats affect them all and direct human stressors are more prevalent in those with higher human populations. To meet global goals for effective conservation and management, there is an urgent need for additional and continued investment in research and management in the Overseas Territories, particularly those that have been of lesser focus