A protocol for developing a complex needs indicator for veterans (CNIV) in the UK

Introduction: The veteran population in the UK has been decreasing, however, there remains a proportion of veterans and their families who continue to experience multiple and complex health, financial, and social needs. The complex problems tend to exacerbate each other and deepen over time if appropriate support is not provided. Identifying the veterans with complex needs is crucial for effective support by military charities and health and social care services. The present research aims to develop a complex needs indicator for the veteran population (CNIV) that will quantify complexity and help to identify the risk of having or developing complex needs. Methods: The development of the CNIV will be informed by the guidance for constructing composite indicators. The data on grant support received by veterans’ beneficiaries from the UK Royal Marine and SSFA charities will be used for designing the indicator and evaluating its robustness. The crucial step in constructing the indicator is assigning weights to different needs and risk factors associated with complex cases. Factor analysis (FA) and analytical network process (ANP) will be used as weighting methods for the analysed variables. Conclusion: The development of CNIV has important implications for research and practice, such as the potential to be used as a screening tool for identifying complex cases, improved provision of the targeted support to veterans, assessing the scope of complex problems among veterans within the country and informing policy makers and a more general audience of the complexity of need within the sector

Parameter distributions for the drag-based modeling of CME propagation

In recent years, ensemble modeling has been widely employed in space weather to estimate uncertainties in forecasts. We here focus on the ensemble modeling of Coronal Mass Ejections (CME) arrival times and arrival velocities using a drag-based model, which is well-suited for this purpose due to its simplicity and low computational cost. Although ensemble techniques have previously been applied to the drag-based model, it is still not clear how to best determine distributions for its input parameters, namely the drag parameter and the solar wind speed. The aim of this work is to evaluate statistical distributions for these model parameters starting from a list of past CME-ICME events. We employ LASCO coronagraph observations to measure initial CME position and speed, and in situ data to associate them with an arrival date and arrival speed. For each event we ran a statistical procedure to invert the model equations, producing parameters distributions as output. Our results indicate that the distributions employed in previous works were appropriately selected, even though they were based on restricted samples and heuristic considerations. On the other hand, possible refinements to the current method are also identified, such as the dependence of the drag parameter distribution on the CME being accelerated or decelerated by the solar wind, which deserve further investigation

AI-ready data in space science and solar physics: problems, mitigation and action plan

In the domain of space science, numerous ground-based and space-borne data of various phenomena have been accumulating rapidly, making analysis and scientific interpretation challenging. However, recent trends in the application of artificial intelligence (AI) have been shown to be promising in the extraction of information or knowledge discovery from these extensive data sets. Coincidentally, preparing these data for use as inputs to the AI algorithms, referred to as AI-readiness, is one of the outstanding challenges in leveraging AI in space science. Preparation of AI-ready data includes, among other aspects: 1) collection (accessing and downloading) of appropriate data representing the various physical parameters associated with the phenomena under study from different repositories; 2) addressing data formats such as conversion from one format to another, data gaps, quality flags and labeling; 3) standardizing metadata and keywords in accordance with NASA archive requirements or other defined standards; 4) processing of raw data such as data normalization, detrending, and data modeling; and 5) documentation of technical aspects such as processing steps, operational assumptions, uncertainties, and instrument profiles. Making all existing data AI-ready within a decade is impractical and data from future missions and investigations exacerbates this. This reveals the urgency to set the standards and start implementing them now. This article presents our perspective on the AI-readiness of space science data and mitigation strategies including definition of AI-readiness for AI applications; prioritization of data sets, storage, and accessibility; and identifying the responsible entity (agencies, private sector, or funded individuals) to undertake the task