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Investigating the long-term public health and co-benefit impacts of an urban greenway intervention in the UK: a natural experiment evaluation - study protocol
No full textIntroduction Urban green and blue space (UGBS) interventions, such as the development of an urban greenway, have the potential to provide public health benefits and multiple co-benefits in the realms of the environment, economy and society. This paper presents the protocol for a 5-year follow-up evaluation of the public health benefits and co-benefits of an urban greenway in Belfast, UK.Methods and analysis The natural experiment evaluation uses a range of systems-oriented and mixed-method approaches. First, using group model building methods, we codeveloped a causal loop diagram with stakeholders to inform the evaluation framework. We will use other systems methods including viable systems modelling and soft systems methodology to understand the context of the system (ie, the intervention) and the stakeholders involved in the development, implementation and maintenance phases. The effectiveness evaluation includes a repeat cross-sectional household survey with a random sample of 1200 local residents (adults aged ≥16 years old) who live within 1 mile of the greenway. The survey is complemented with administrative data from the National Health Service. For the household survey, outcomes include physical activity, mental well-being, quality of life, social capital, perceptions of environment and biodiversity. From the administrative data, outcomes include prescription medications for a range of non-communicable diseases such as cardiovascular disease, type II diabetes mellitus, chronic respiratory and mental health conditions. We also investigate changes in infectious disease rates, including COVID-19, and maternal and child health outcomes such as birth weight and gestational diabetes. A range of economic evaluation methods, including a cost-effectiveness analysis and social return on investment (SROI), will be employed. Findings from the household survey and administrative data analysis will be further explored in focus groups with a subsample of those who complete the household survey and the local community to explore possible mechanistic pathways and other impacts beyond those measured. Process evaluation methods include intercept surveys and direct observation of the number and type of greenway visitors using the Systems for Observing Play and Recreation in Communities tool. Finally, we will use methods such as weight of evidence, simulation and group model building, each embedding participatory engagement with stakeholders to help us interpret, triangulate and synthesise the findings.Ethics and dissemination To our knowledge, this is one of the first natural experiments with a 5-year follow-up evaluation of an UGBS intervention. The findings will help inform future policy and practice on UGBS interventions intended to bring a range of public health benefits and co-benefits. Ethics approval was obtained from the Medicine, Health and Life Sciences Research Ethics Committee prior to the commencement of the study. All participants in the household survey and focus group workshops will provide written informed consent before taking part in the study. Findings will be reported to (1) participants and stakeholders; (2) funding bodies supporting the research; (3) local, regional and national governments to inform policy; (4) presented at local, national and international conferences and (5) disseminated by peer-review publications.<br/
Duke activity status index is not predictive of outcomes after kidney transplantation: a retrospective observational study
No full textBackgroundReduced functional capacity increases the risk of adverse outcomes after kidney transplantation. The Duke Activity Status Index is a measurement of physical function, previously reported as being predictive of adverse outcomes after major non-cardiac surgery. This study assessed the ability of the Duke Activity Status Index to predict adverse outcomes for patients undergoing kidney transplantation.MethodsAdult kidney transplant recipients with a Duke Activity Status Index calculated at time of listing for transplantation in Northern Ireland between 2019 and 2024 were analysed. Dichotomous outcomes (delayed graft function, unplanned critical care admission, 30-day hospital re-admission, 30-day severe postoperative complication, 30-day cardiovascular complication) were analysed using multivariate logistic regression. Post-transplant length of stay was assessed using multivariate linear regression. All-cause mortality and death-censored graft loss were evaluated using Cox proportional hazard regression models.ResultsData was available for 408 kidney transplant recipients. Duke Activity Status Index was not predictive of delayed graft function (aOR 0.99 (95% CI 0.66–1.01) p = 0.359), unplanned critical care admission (aOR1.00 (95% CI 0.97–1.04), p = 0.866), length-of-stay post-transplant, 30-day hospital re-admission (aOR1.01 (95% CI 0.99–1.03), p = 0.457), 30-day severe postoperative complication (aOR 1.01 (95% CI 0.99–1.03) p = 0.489), 30-day cardiovascular complication (aOR 0.99 (95% CI 0.93–1.06), p = 0.850), all-cause mortality (aHR 1.00 (0.96–1.04), p = 0.89) or death-censored graft loss (aHR 0.97 (95% CI 0.93–1.01), p = 0.14).ConclusionsIn this cohort, the Duke Activity Status Index was not an independent predictor of short or long-term adverse outcomes following kidney transplantation. These findings suggest that the Duke Activity Status Index may have limited utility in assessing functional capacity in waitlisted kidney transplant candidates.<br/
Curb EU enthusiasm: how politicisation shapes bureaucratic responsiveness
No full textInternational institutions are often challenged for being detached from the citizens. Focusing on the European Union, this article studies whether this is the case and, if so, when the European Commission responds to public opinion when pursuing policy integration. It argues that the Commission has legitimacy incentives encouraging it to be responsive but politicisation can suppress responsiveness by transmitting competing demands on the Commission from the member states’ citizens. To test these arguments, the contribution applied automated text analysis to estimate the European Union (EU) authority expansion entailed in the Commission’s legislative proposals between 2009 and 2019 and analysed its correspondence with public preferences over EU policy action across the EU states, measured using the Eurobarometer. The results lend support to the hypotheses and suggest that politicisation can undermine the responsiveness of international institutions
Quantum-enhanced DRL optimization for DoA estimation and task offloading in ISAC systems
No full textThis work proposes a quantum-aided deep reinforcement learning (DRL) framework designed to enhance the accuracy of direction-of-arrival (DoA) estimation and the efficiency of computational task offloading in integrated sensing and communication systems. Traditional DRL approaches face challenges in handling high-dimensional state spaces and ensuring convergence to optimal policies within complex operational environments. The proposed quantum-aided DRL framework that operates in a military surveillance system exploits quantum computing’s parallel processing capabilities to encode operational states and actions into quantum states, significantly reducing the dimensionality of the decision space. For the very first time in literature, we propose a quantum-enhanced actor-critic method, utilizing quantum circuits for policy representation and optimization. Through comprehensive simulations, we demonstrate that our framework improves DoA estimation accuracy by 91.66% and 82.61% over existing DRL algorithms with faster convergence rate, and effectively manages the trade-off between sensing and communication and optimizing task offloading decisions under stringent ultra-reliable low-latency communication requirements. Comparative analysis also reveals that our approach reduces the overall task offloading latency by 43.09% and 32.35% compared to the DRL-based deep deterministic policy gradient and proximal policy optimization algorithms, respectively stringent ultra-reliable low-latency communication requirements. Comparative analysis also reveals that our approach reduces the overall task offloading latency by 43.09% and 32.35% compared to the DRL-based deep deterministic policy gradient and proximal policy optimization algorithms, respectively
Detection of sugar syrup adulteration in UK honey using DNA barcoding
No full textHoney is a valuable and nutritious food product, but it is at risk to fraudulent practices such as the addition of cheaper syrups including corn, rice, and sugar beet syrup. Honey authentication is of the utmost importance, but current methods are faced with challenges due to the large variations in natural honey composition (influenced by climate, seasons and bee foraging), or the incapability to detect certain types of plant syrups to confirm the adulterant used. Molecular methods such as DNA barcoding have shown great promise in identifying plant DNA sources in honey and could be applied to detect plant-based sugars used as adulterants. In this work DNA barcoding was successfully used to detect corn and rice syrup adulteration in spiked UK honey with novel DNA markers. Different levels of adulteration were simulated (1 – 30%) with a range of different syrup and honey types, where adulterated honey was clearly separated from natural honey even at 1% adulteration level. Moreover, the test was successful for multiple syrup types and effective on honeys with different compositions. These results demonstrated that DNA barcoding could be used as a sensitive and robust method to detect common sugar adulterants and confirm syrup species origin in honey, which can be applied alongside current screening methods to improve existing honey authentication tests.<br/
Efficient integer-only-inference of gradient boosting decision trees on low-power devices
No full textThere is increasingly interest in developing embedded machine learning hardware as it can offer better performance in terms of privacy, bandwidth efficiency, and scalability. Gradient-boosted decision trees (GBDT) represent a strong candidate as they employ less complex logic, but their efficient implementation in field programmable gate array (FPGA) needsto be explored in detail. In this paper, we propose sophisticated quantisation approaches to balance the dual goals of efficiency and performance. In particular, we introduce quantisation-aware training of GBDT for integer-only and binary arithmetic. Results are presented for implementations on a Zynq UltraScale+ MPSoC FPGA with the best design using only 170 Look-up Tables and 233 flip-flops at a clock speed of 724 MHz. Implementations focused on network intrusion detection and jet substructure classification for large-scale physics experiments are explored. An order of magnitude less FPGA resources are used whilst offering extremely high throughput rate and maintaining accuracy. Code is available at https://github.com/malsharari/QATGBDT
Network governance and reflexivity in fisheries management: a case study of Northern Ireland
No full textNetwork governance arrangements, wherein resource users are empowered, are vital for ocean sustainability. These arrangements are critical to implementing innovative practices, particularly those developed to address the negative impacts of top-down management. The adoption of networked governance can foster resource management capacity and equitable decision-making. Research has focused on social ties within networks but has provided a somewhat limited account of how networks evolve to instil novel practices. To implement new practices, networks must engage in reflexive governance. Reflexivity is underpinned by actors’ receptivity to change and network learning. Actors’ receptivity to change relates to whether they acknowledge past failures and how novel management ideas become accepted. Network learning is the degree of change instigated by actors’ reflexive processes. Drawing on the receptivity and network learning theories, and informed by semi-structured interviews, an assessment was conducted on how a fisheries management network in Northern Ireland has reflexively evolved to implement new practices. Findings illustrate that the network has reflexively redesigned how they collaborate, share knowledge, and contribute to policymaking. Demonstrating single– and double-loop learning, these developments have been derived from reflections on the failure of past processes and the instigation of more co-creative processes that benefit all network actors.<br/