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Methods for quantifying methane emissions using unmanned aerial vehicles: a reviewMethane is an important greenhouse gas, emissions of which have vital consequences for global climate change. Understanding and quantifying the sources (and sinks) of atmospheric methane is integral for climate change mitigation and emission reduction strategies, such as those outlined in the 2015 UN Paris Agreement on Climate Change. There are ongoing international efforts to constrain the global methane budget, using a wide variety of measurement platforms across a range of spatial and temporal scales. The advancements in unmanned aerial vehicle (UAV) technology over the past decade have opened up a new avenue for methane emission quantification. UAVs can be uniquely equipped to monitor natural and anthropogenic emissions at local scales, displaying clear advantages in versatility and manoeuvrability relative to other platforms. Their use is not without challenge, however: further miniaturization of high-performance methane instrumentation is needed to fully use the benefits UAVs afford. Developments in the models used to simulate atmospheric transport and dispersion across small, local scales are also crucial to improved flux accuracy and precision. This paper aims to provide an overview of currently available UAV-based technologies and sampling methodologies which can be used to quantify methane emission fluxes at local scales. This article is part of a discussion meeting issue 'Rising methane: is warming feeding warming? (part 1)'.
Developmentally appropriate transitional care during the Covid-19 pandemic for young people with juvenile-onset rheumatic and musculoskeletal diseases: the rationale for a position statement.<h4>Background</h4>The importance of developmentally appropriate transitional care in young people with juvenile-onset rheumatic and musculoskeletal disease is well recognised. The Paediatric Rheumatology European Society (PReS) / European League Against Rheumatism (EULAR) Taskforce has developed international recommendations and standards for transitional care and a growing evidence base supports the positive benefits of such care. However, there is also evidence that universal implementation has yet to be realised. In 2020, against this background the COVID-19 pandemic arrived with significant impact on all our lives, young and old, patient, public and professional alike. The unfortunate reality of the pandemic with potential for unfavourable outcomes on healthcare provision during transition was acknowledged by the PReS working groups in a position statement to support healthcare professionals, young people and their caregivers.<h4>Aim</h4>The aim of this review is to present the literature which provides the rationale for the recommendations in the PReS Position Statement. The following areas are specifically addressed: the prime importance of care coordination; the impact of the pandemic on the various aspects of the transition process; the importance of ensuring continuity of medication supply; the pros and cons of telemedicine with young people; ensuring meaningful involvement of young people in service development and the importance of core adolescent health practices such as routine developmental assessment psychosocial screening and appropriate parental involvement during transitional care.
Accuracy of emergency medical services (EMS) telephone triage in identifying acute coronary syndrome (ACS) for patients with chest pain: a systematic literature review.<h4>Objective</h4>To systematically appraise the available evidence to determine the accuracy of decision aids for emergency medical services (EMS) telephone triage of patients with chest pain suspected to be caused by acute coronary syndrome (ACS) or life-threatening conditions.<h4>Design</h4>Systematic review.<h4>Data sources</h4>Electronic searches were performed in Embase 1974, Medline 1946 and CINAHL 1937 databases from 3 March 2020 to 4 March 2020.<h4>Eligibility criteria</h4>The review included all types of original studies that included adult patients (>18 years) who called EMS with a primary complaint of chest pain and evaluated dispatch triage priority by telephone. Outcomes of interest were a final diagnosis of ACS, acute myocardial infarction or other life-threatening conditions.<h4>Data extraction and synthesis</h4>Two authors independently extracted data on study design, population, study period, outcome and all data for assessment of accuracy, including cross-tabulation of triage priority against the outcomes of interest. Risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 assessment tool.<h4>Results</h4>Searches identified 553 papers, of which 3 were eligible for inclusion. Those reports described the evaluation of three different prediction models with variation in the variables used to detect ACS. The overall results showed that dispatch triage tools have good sensitivity to detect ACS and life-threatening conditions, even though they are used to triage signs and symptoms rather than diagnosing the patients. On the other hand, prediction models were built to detect ACS and life-threatening conditions, and therefore, prediction models showed better sensitivity and negative predictive value than dispatch triage tools.<h4>Conclusion</h4>We have identified three prediction models for telephone triage of patients with chest pain. While they have been found to have greater accuracy than standard EMS dispatch systems, prospective external validation is essential before clinical use is considered.<h4>Prospero registration number</h4>This systematic review was pre-registered on the International prospective register of systematic reviews (PROSPERO) database (reference CRD42020171184).
Role of emotional intelligence in effective nurse leadership.Emotionally intelligent leaders demonstrate a sensitivity to their own and other people's psychological health and well-being, directing others towards common goals while developing effective personal relationships with their colleagues and team members. Emotional intelligence is particularly relevant in the context of the coronavirus disease 2019 pandemic, where nurse leaders need to demonstrate this skill when supporting their teams to manage high levels of stress, exhaustion and the risk of moral injury. This article explores emotional intelligence, discusses its importance as a characteristic of effective nurse leaders and managers, and suggests practical activities that leaders can undertake to develop their emotional intelligence skills. [Abstract copyright: © 2021 RCN Publishing Company Ltd. All rights reserved. Not to be copied, transmitted or recorded in any way, in whole or part, without prior permission of the publishers.]
A Novel Ship Collision Avoidance Awareness Approach for Cooperating Ships Using Multi-Agent Deep Reinforcement LearningShips are special machineries with large inertias and relatively weak driving forces. Simulating the manual operations of manipulating ships with artificial intelligence (AI) and machine learning techniques becomes more and more common, in which avoiding collisions in crowded waters may be the most challenging task. This research proposes a cooperative collision avoidance approach for multiple ships using a multi-agent deep reinforcement learning (MADRL) algorithm. Specifically, each ship is modeled as an individual agent, controlled by a Deep Q-Network (DQN) method and described by a dedicated ship motion model. Each agent observes the state of itself and other ships as well as the surrounding environment. Then, agents analyze the navigation situation and make motion decisions accordingly. In particular, specific reward function schemas are designed to simulate the degree of cooperation among agents. According to the International Regulations for Preventing Collisions at Sea (COLREGs), three typical scenarios of simulation, which are head-on, overtaking and crossing, are established to validate the proposed approach. With sufficient training of MADRL, the ship agents were capable of avoiding collisions through cooperation in narrow crowded waters. This method provides new insights for bionic modeling of ship operations, which is of important theoretical and practical significance.