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We report on the development of machine learning models for classifying C100 superconducting radio-frequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. CEBAF is a continuous-wave recirculating linac utilizing 418 SRF cavities to accelerate electrons up to 12 GeV through five passes. Of these, 96 cavities (12 cryomodules) are designed with a digital low-level rf system configured such that a cavity fault triggers waveform recordings of 17 rf signals for each of the eight cavities in the cryomodule. Subject matter experts are able to analyze the collected time-series data and identify which of the eight cavities faulted first and classify the type of fault. This information is used to find trends and strategically deploy mitigations to problematic cryomodules. However, manually labeling the data is laborious and time consuming. By leveraging machine learning, near real-time—rather than postmortem—identification of the offending cavity and classification of the fault type has been implemented. We discuss performance of the machine learning models during a recent physics run. Results show the cavity identification and fault classification models have accuracies of 84.9% and 78.2%, respectively.

The ideal of the self-driving car replaces an error-prone human with an infallible, artificially intelligent driver. This narrative of autonomy promises liberation from the downsides of automobility, even if that means taking control away from autonomous, free-moving individuals. We look behind this narrative to understand the attachments that so-called ‘autonomous’ vehicles (AVs) are likely to have to the world. Drawing on 50 interviews with AV developers, researchers and other stakeholders, we explore the social and technological attachments that stakeholders see inside the vehicle, on the road and with the wider world. These range from software and hardware to the behaviours of other road users and the material, social and economic infrastructure that supports driving and self-driving. We describe how innovators understand, engage with or seek to escape from these attachments in three categories: ‘brute force’, which sees attachments as problems to be solved with more data, ‘solve the world one place at a time’, which sees attachments as limits on the technology’s reach and ‘reduce the complexity of the space’, which sees attachments as solutions to the problems encountered by technology developers. Understanding attachments provides a powerful way to anticipate various possible constitutions for the technology.

The UK Government has endorsed the case for autonomous vehicle (AV) technology and its economic benefits in its industrial strategies since 2013. In late 2016 the Science and Technology Committee in the House of Lords (the legislature’s upper chamber) conducted an Inquiry into the policy. We conduct a content analysis of the text corpus of the Inquiry. Drawing from theories of sociotechnical change we explore how it contributes to building a vision of a future AV world embedded in a national economic and technological project. The technology is framed as a solution to societal grand challenges and the Inquiry corpus is dominated by actors committed to the project. Alternative visions, including sceptical interpretations, are present in the corpus, but rare, reflecting the selection process for contributions to the Inquiry. Predominantly, the corpus represents the public as deficient: dangerous drivers, unaware of promised benefits and unduly anxious about the unfamiliar. Their views are marginal in this Parliamentary Inquiry’s findings. AV technology is one of several possible means to pursue wider mobility policy goals of greater safety, affordability, access and sustainability. Our analysis suggests that the pursuit of an AV future risks becoming a goal in itself instead of a means to these broader societal goals.

We present the initial results of a proof-of-concept ‘smart alarm’ for the Continuous Electron Beam Accelerator Facility injector beamline at Jefferson Lab. To minimize machine downtime and improve operational efficiency, an autonomous alarm system able to identify and diagnose unusual machine states is needed. Our approach leverages a trained neural network capable of alerting operators (a) when an anomalous condition exists in the beamline and (b) identifying the element setting that is the root cause. The tool is based on an inverse model that maps beamline readings (diagnostic readbacks) to settings (beamline attributes operators can modify). The model takes as input readings from the machine and computes machine settings which are compared to control setpoints. Instances where predictions differ from setpoints by a user-defined threshold are flagged as anomalous. Given data corresponding to 354 anomalous injector configurations, the model can narrow the root cause of an anomalous condition to three potential candidates with 94.6% accuracy. Furthermore, compared to the current method of identifying anomalous conditions which raises an alarm when machine parameters drift outside their normal tolerances, the data-driven model can identify 83% more anomalous conditions.

Objective To evaluate the effectiveness of a population based, multifaceted shared care intervention for late life depression in residential care. Design Randomised controlled trial, with control and intervention groups studied one after the other and blind follow up after 9.5 months. Setting Population of residential facility in Sydney living in self care units and hostels. Participants 220 depressed residents aged ≥65 without severe cognitive impairment. Intervention The shared care intervention included: (a) multidisciplinary consultation and collaboration, (b) training of general practitioners and carers in detection and management of depression, and (c) depression related health education and activity programmes for residents. The control group received routine care. Main outcome measure Geriatric depression scale. Results Intention to treat analysis was used. There was significantly more movement to \"less depressed\" levels of depression at follow up in the intervention than control group (Mantel-Haenszel stratification test, P = 0.0125). Multiple linear regression analysis found a significant intervention effect after controlling for possible confounders, with the intervention group showing an average improvement of 1.87 points on the geriatric depression scale compared with the control group (95% confidence interval 0.76 to 2.97, P = 0.0011). Conclusions The outcome of depression among elderly people in residential care can be improved by multidisciplinary collaboration, by enhancing the clinical skills of general practitioners and care staff, and by providing depression related health education and activity programmes for residents.

This position paper presents a vision for self-driving particle accelerators that operate autonomously with minimal human intervention. We propose that future facilities be designed through artificial intelligence (AI) co-design, where AI jointly optimizes the accelerator lattice, diagnostics, and science application from inception to maximize performance while enabling autonomous operation. Rather than retrofitting AI onto human-centric systems, we envision facilities designed from the ground up as AI-native platforms. We outline nine critical research thrusts spanning agentic control architectures, knowledge integration, adaptive learning, digital twins, health monitoring, safety frameworks, modular hardware design, multimodal data fusion, and cross-domain collaboration. This roadmap aims to guide the accelerator community toward a future where AI-driven design and operation deliver unprecedented science output and reliability.

Background. The aim was to examine the temporal relationships over 10 months between cannabis use and symptoms of psychosis and depression in people with schizophrenia and related disorders. The design was a prospective study of 101 patients with schizophrenia and related disorders who were assessed monthly over 10 months on medication compliance, cannabis and other drug use, symptoms of depression and symptoms of psychosis. Method. Linear regression methods to assess relationships between cannabis use and symptoms of psychosis and depression while adjusting for serial dependence, medication compliance and other demographic and clinical variables. Results. Cannabis use predicted a small but statistically significant increase in symptoms of psychosis, but not depression, after controlling for other differences between cannabis users and non-users. Symptoms of depression and psychosis did not predict cannabis use. Conclusion. Continued cannabis use by persons with schizophrenia predicts a small increase in psychotic symptom severity but not vice versa.

We propose a small, shared core ontology for particle accelerators that provides a semantic backbone for interoperable data and workflows across facilities. The ontology names key device types, signals, parameters, and regions, and relates them through explicit properties (e.g., hasSetpoint, hasReadback, partOf). Each site contributes a lightweight facility bundle, a profile that maps local conventions into the shared vocabulary plus data slices that instantiate those mappings, without renaming channel addresses or changing existing systems. Using standard W3C technologies, the approach supports both sparse and rich descriptions. We demonstrate the idea on two beamline segments at different laboratories. A single semantic query is expressed once and evaluated against both knowledge bases, returning the locally correct PVs. The ontology thereby enables not only portable workflows but also interoperable data, since measurements and catalogs are annotated with shared semantics rather than facility-specific names. The framework complements, rather than replaces, existing middle layers and lattice/data standards, and it creates a stable foundation for reusable tools and agentic workflows.

We report on the development of machine learning models for classifying C100 superconducting radio-frequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. CEBAF is a continuous-wave recirculating linac utilizing 418 SRF cavities to accelerate electrons up to 12 GeV through five passes. Of these, 96 cavities (12 cryomodules) are designed with a digital low-level rf system configured such that a cavity fault triggers waveform recordings of 17 rf signals for each of the eight cavities in the cryomodule. Subject matter experts are able to analyze the collected time-series data and identify which of the eight cavities faulted first and classify the type of fault. This information is used to find trends and strategically deploy mitigations to problematic cryomodules. However, manually labeling the data is laborious and time consuming. By leveraging machine learning, near real-time—rather than postmortem—identification of the offending cavity and classification of the fault type has been implemented. We discuss performance of the machine learning models during a recent physics run. Results show the cavity identification and fault classification models have accuracies of 84.9% and 78.2%, respectively.

Objective: To describe a population-based, multifaceted shared-care intervention for late-life depression in residential care as a new model of geriatric practice, to outline its development and implementation, and to describe the lessons learned during the implementation process. Setting: A large continuing-care retirement community in Sydney, Australia, providing three levels of care (independent living units, assisted-living complexes, and nursing homes). Participants:) The intervention was implemented for the entire non-nursing home population (residents in independent and assisted living: N = 1,466) of the facility and their health care providers. Of the 1,036 residents who were eligible and agreed to be interviewed, 281 (27.1%) were classified as depressed according to the Geriatric Depression Scale. Intervention Description: The intervention included: (a) multidisciplinary collaboration between primary care physicians, facility health care providers, and the local psychogeriatric service; (b) trainning for primary care physicians and other facility health care providers about detecting and managing depression; and (c) depression-related health education/promotion programs for residents. Conclusions: The intervention was widely accepted by residents and their health care providers, and was sustained and enhanced by the facility after the completion of the study. It is possible to implement and sustain a multifaceted shared-care intervention for late-life depression in a residential care facility where local psychogeriatric services are scarce, staff-to-resident ratios are low, and the needs of depressed to residents are substantial.