Rapid-Chill Cryogenic Coaxial Direct-Acting Solenoid Valve

A commercially available cryogenic direct- acting solenoid valve has been modified to incorporate a rapid-chill feature. The net effect of the modifications is to divert some of the cryogenic liquid to the task of cooling the remainder of the cryogenic liquid that flows to the outlet. Among the modifications are the addition of several holes and a gallery into a valve-seat retainer and the addition of a narrow vent passage from the gallery to the atmosphere

Visions for a walking and cycling focussed urban transport system

Walking and cycling can make a considerable contribution to sustainable transport goals, building healthier and more sustainable communities and contributing to traffic and pollution reduction. There have been many national and local initiatives to promote walking and cycling, but without a long term vision and consistent strategy it is difficult to see how a significant change may be achieved. This paper presents three alternative visions for the role of walking and cycling in urban areas for the year 2030: each vision illustrates a ‘desirable’ walking- and cycling-oriented transport system against a different ‘exogenous social background’. These visions have been developed through a process of expert discussion and review and are intended to provide a stimulus for debate on the potential for and desirability of such alternative futures. Each is based on the UK and represents a substantial change to the current situation: in particular, each of the visions presents a view of a society where walking and cycling are considerably more important than is currently the case and where these modes cater for a much higher proportion of urban transport needs than at present. The visions show pictures of urban environments where dependence on motor vehicles has been reduced, in two of the visions to very low levels. The methodological approach for devising visions is informed by work on ‘utopian thinking’: a key concept underlying this approach is one of viewing the future in social constructivist terms (i.e. the future is what ‘we’, as a society, make it) rather than considering the future as something that can be ‘scientifically’ predicted by the extrapolation of current trends

In a New Land:Mobile Phones, Amplified Pressures and Reduced Capabilities

Framed within the theoretical lens of positive and negative security, this paper presents a study of newcomers to Sweden and the roles of mobile phones in the establishment of a new life. Using creative engagement methods through a series of workshops, two researchers engaged 70 adult participants enrolled into further education colleges in Sweden. Group narratives about mobile phone use were captured in creative outputs, researcher observations and notes and were analysed using thematic analysis. Key findings show that the mobile phone offers security for individuals and a safe space for newcomers to establish a new life in a new land as well as capitalising on other spaces of safety, such as maintaining old ties. This usage produces a series of threats and vulnerabilities beyond traditional technological security thinking related to mobile phone use. The paper concludes with recommendations for policies and support strategies for those working with newcomers

Federated Benchmarking of Medical Artificial Intelligence With MedPerf

Medical artificial intelligence (AI) has tremendous potential to advance healthcare by supporting and contributing to the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving both healthcare provider and patient experience. Unlocking this potential requires systematic, quantitative evaluation of the performance of medical AI models on large-scale, heterogeneous data capturing diverse patient populations. Here, to meet this need, we introduce MedPerf, an open platform for benchmarking AI models in the medical domain. MedPerf focuses on enabling federated evaluation of AI models, by securely distributing them to different facilities, such as healthcare organizations. This process of bringing the model to the data empowers each facility to assess and verify the performance of AI models in an efficient and human-supervised process, while prioritizing privacy. We describe the current challenges healthcare and AI communities face, the need for an open platform, the design philosophy of MedPerf, its current implementation status and real-world deployment, our roadmap and, importantly, the use of MedPerf with multiple international institutions within cloud-based technology and on-premises scenarios. Finally, we welcome new contributions by researchers and organizations to further strengthen MedPerf as an open benchmarking platform

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14