2,546 research outputs found
Integrating expert-based objectivist and nonexpert-based subjectivist paradigms in landscape assessment
This thesis explores the integration of objective and subjective measures of landscape aesthetics, particularly focusing on crowdsourced geo-information. It addresses the increasing importance of considering public perceptions in national landscape governance, in line with the European Landscape Convention's emphasis on public involvement. Despite this, national landscape assessments often remain expert-centric and top-down, facing challenges in resource constraints and limited public engagement. The thesis leverages Web 2.0 technologies and crowdsourced geographic information, examining correlations between expert-based metrics of landscape quality and public perceptions. The Scenic-Or-Not initiative for Great Britain, GIS-based Wildness spatial layers, and LANDMAP dataset for Wales serve as key datasets for analysis.
The research investigates the relationships between objective measures of landscape wildness quality and subjective measures of aesthetics. Multiscale geographically weighted regression (MGWR) reveals significant correlations, with different wildness components exhibiting varying degrees of association. The study suggests the feasibility of incorporating wildness and scenicness measures into formal landscape aesthetic assessments. Comparing expert and public perceptions, the research identifies preferences for water-related landforms and variations in upland and lowland typologies. The study emphasizes the agreement between experts and non-experts on extreme scenic perceptions but notes discrepancies in mid-spectrum landscapes. To overcome limitations in systematic landscape evaluations, an integrative approach is proposed. Utilizing XGBoost models, the research predicts spatial patterns of landscape aesthetics across Great Britain, based on the Scenic-Or-Not initiatives, Wildness spatial layers, and LANDMAP data. The models achieve comparable accuracy to traditional statistical models, offering insights for Landscape Character Assessment practices and policy decisions. While acknowledging data limitations and biases in crowdsourcing, the thesis discusses the necessity of an aggregation strategy to manage computational challenges. Methodological considerations include addressing the modifiable areal unit problem (MAUP) associated with aggregating point-based observations. The thesis comprises three studies published or submitted for publication, each contributing to the understanding of the relationship between objective and subjective measures of landscape aesthetics. The concluding chapter discusses the limitations of data and methods, providing a comprehensive overview of the research
The PREVENT Study: Preventing hospital admissions attributable to gout
BackgroundGout is the most common form of inflammatory arthritis, affecting 1 in 40 people in the UK. Despite highly effective treatments, hospital admissions for gout flares have doubled in England over the last 20 years. Many of these admissions may have been prevented if optimal gout management had been delivered to patients.Objectives1. Describe the epidemiology of gout management in primary and secondary care in the UK.2. Develop an intervention package for implementation during hospitalisations for gout flares, with the aim of improving care and reducing hospitalisations.3. Implement and evaluate this intervention in people hospitalised for gout.MethodsI used population-level health datasets (CPRD, OpenSAFELY, NHS Digital Hospital Episode Statistics) to evaluate outcomes for people with incident gout diagnoses over a 20-year period. I used multivariable regression and survival modelling to analyse factors associated with outcomes, including: i) initiation of urate-lowering therapies (ULT); ii) attainment of serum urate targets; and iii) hospitalisations for gout flares.With extensive stakeholder input, I developed an evidence-based intervention package to optimise hospital gout care. This incorporated the findings of a systematic literature review and process mapping of the admitted patient journey in a cohort of hospitalised gout patients. My intervention consisted of a care pathway, based upon British (BSR), European (EULAR) and American (ACR) gout management guidelines, which encouraged ULT initiation prior to discharge, followed by a nurse-led, post-discharge review to facilitate handover to primary care. I implemented this intervention in patients hospitalised for gout flares at King’s College Hospital over a 12-month period, and evaluated outcomes including ULT initiation, urate target attainment and re-admission rates.ResultsIn the UK, between 2004 and 2020, I showed that only 29% of patients with gout were initiated on ULT within 12 months of diagnosis, while only 36% attained urate targets. No significant improvements in these outcomes were observed after publication of updated BSR and EULAR gout management guidelines. Comorbidities, including chronic kidney disease, heart failure and obesity, associated with increased odds of ULT initiation but decreased odds of attaining urate targets. For patients who were diagnosed with gout during the COVID-19 pandemic, I showed that ULT initiation improved modestly, relative to before the pandemic, while urate target attainment trends were similar. Underlying these trends was a 31% decrease in incident gout diagnoses in England during the first year of the pandemic.Using linked primary and secondary care data, I showed that the risk of hospitalisations for gout flares is greatest within the first 6 months after diagnosis. ULT initiation is associated with more hospitalisations for flares within the first 6 months of diagnosis, but a reduced risk of hospitalisations beyond 12 months; particularly when urate targets are attained.After process mapping the admitted patient journey and systematically appraising the evidence base, I developed and implemented a multi-faceted intervention at King’s College Hospital, with the aim of improving hospital gout care. Following implementation of this intervention, the proportion of hospitalised gout patients who initiated ULT increased from 49% to 92%; more patients achieved serum urate targets; and there were 38% fewer repeat hospitalisations for gout flares.ConclusionsAt a population level, ULT initiation and urate target attainment remain sub-optimal for people with gout in the UK, despite updated management guidelines. Initiation of ULT is associated with long-term reductions in hospitalisations for flares; however, only a minority of patients hospitalised for gout flares are initiated on ULT. After designing and implementing a strategy to optimise hospital gout care, over 90% of patients were initiated on ULT, urate target attainment improved, and repeat hospitalisations decreased. My findings suggest that improved primary-secondary care integration is essential if we are to reverse the epidemic of gout hospitalisations
Musculoskeletal complaints in primary care:Constraining healthcare costs, rethinking the deployment of healthcare professionals
Worldwide policy makers are challenged to account for rising healthcare costs and increased healthcare demand. Also, in the Netherlands there is a growing concern how to maintain high-quality and accessible care while keeping costs in check. Access to care is under pressure as the demand for care is rising fast, due to an aging population and an increasing number of chronically ill people. Not only at the policy level, but also in clinical practice challenges exist. The workload in the health care sector is high, causing health workers, such as general practitioners (GPs), to leave this sector. To keep costs in check available resources need be allocated as efficiently as possible. A good starting point for evaluating healthcare costs may be assessing large patient groups that are responsible for high resource use and costs, such as patients with musculoskeletal conditions treated in general practice. Another point may be identifying prognostic factors for higher healthcare costs. Besides lowering costs, it is also of importance to keep GP care accessible by lowering GPs’ workload. One of the ways to address GPs’ high workload is task reallocation. Internationally, positive effects have been found for an Advanced Physiotherapy Practitioner (APP) model of care, in which APPs take over tasks from a physician in the care for patients with musculoskeletal conditions. This model of care could potentially be of value in reducing the workload of Dutch GPs and keeping GP care accessible. Besides lowering healthcare cost and decreasing GPs’ workload maintaining good quality care is essential. One of the most widely used Patient Reported Outcome Measures (PROMs) in assessing quality of healthcare is the EQ-5D, a preference-based measurement instrument that measures health related quality of life and is used to estimate utility values that represent the preferences of the general population of a country for given health states. These utility values are needed for estimating Quality-Adjusted Life-Years (QALYs) in cost effectiveness analysis. However, quality-of-life measurements are generally not available when data are collected for clinical purposes, such as data from GP electronic medical records. Therefore, researchers are exploring ways to estimate EQ-5D based utility values by means of outcomes on other available health related outcome measures. This thesis aimed to explore some of the challenges in Dutch primary care by evaluating 1) healthcare utilization and associated cost of GP-guided care in patients with musculoskeletal complaints, 2) the introduction of an APP model of care, and 3) different approaches to estimate missing EQ-5D based utility values
Multidisciplinary perspectives on Artificial Intelligence and the law
This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio
Empirical modelling of the solar spectral influence on photovoltaic devices for improved performance forecasting
Photovoltaic performance modelling is essential for the successful development of PV systems. Accurate modelling can inform system design and financing prior to construction, help with fault detection during operation, and improve the grid penetration of PV energy.
Whereas the models to account for the effects of broadband irradiance, temperature, and so forth on PV performance are well established, those for the influence of the solar spectrum, known as spectral correction functions (SCFs), suffer a range of limitations. Existing models are typically based on proxy variables used to represent the solar spectrum, which are restricted in the amount of information they contain on the prevailing spectral irradiance conditions. Furthermore, validation of these models is restricted to climates that are not representative of the UK, where a broader range of spectral irradiance conditions is experienced due to its high northern latitude and frequent overcast or partially overcast skies.
Some studies have explored the possibility of characterising measured spectra with parameters such as the average photon energy to develop SCFs. However, these studies are limited in terms of their validation scope, such as duration of field data and types of PV module, and extension to a predictive model. In this project, two new SCFs are developed and validated in two distinct climate regions for multiple PV technologies. The first is based on the average photon energy alone (f(APE)), while the second is based on both the average photon energy and the depth of the 650--670nm water absorption band (f(APE,e)). Using data from Go (Golden, Colorado, USA), the former is shown to cut the prediction error for aSi modules by around 40% relative to a single-variable air mass SCF and a double-variable air mass and clearness index SCF. The latter, f(APE,e), addresses issues raised in the literature regarding the reliability of APE as a spectral characterisation index. Using the same data, f(APE,e) is shown to cut the prediction error by up to 60% with respect to a comparable multivariable proxy SCF based on the air mass and atmospheric precipitable water content.
These results are also validated at a new test site built at the University of Nottingham as part of this project. Although the overall errors are greater due to site-specific system characteristics, the relative improvements achieved by the APE-based models with respect to the proxy-based models are maintained in both climate regions.
The proposed spectral correction approaches can be integrated into wider PV performance models to improve their performance forecasting accuracy
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
- …