University of Lincoln

About: University of Lincoln is a education organization based out in Lincoln, Lincolnshire, United Kingdom. It is known for research contribution in the topics: Population & Context (language use). The organization has 2341 authors who have published 7025 publications receiving 124797 citations.

The Marketisation of Charities in England and Wales

Abstract: Much has been written about the reasons for and impact of marketisation on charities, their clients, and wider civil society. A central component of the marketisation thesis is that charities are substituting grants and donations with commercial revenue. However, there is no consensus in the existing literature as to whether the two sources of revenue are substitutes or complementary. This paper shows that between 2003 and 2007 there was a significant increase in the proportion of overall revenue attracted from commercial sources by charities in England and Wales. Using our preferred generalised method of moments estimation model we show that the annual persistence of commercial revenue overtime was 44 %. In particular, a +10 % change in grants and donations was associated with a −3.1 % change in commercial revenue.

Unravelling the scientific debate on how to address wolf-dog hybridization in Europe

Abstract: Anthropogenic hybridization is widely perceived as a threat to the conservation of biodiversity. Nevertheless, to date, relevant policy and management interventions are unresolved and highly convoluted. While this is due to the inherent complexity of the issue, we hereby hypothesize that a lack of agreement concerning management goals and approaches, within the scientific community, may explain the lack of social awareness on this phenomenon, and the absence of effective pressure on decision-makers. By focusing on wolf x dog hybridization in Europe, we hereby (a) assess the state of the art of issues on wolf x dog hybridization within the scientific community, (b) assess the conceptual bases for different viewpoints, and (c) provide a conceptual framework aiming at reducing the disagreements. We adopted the Delphi technique, involving a three-round iterative survey addressed to a selected sample of experts who published at Web of Science listed journals, in the last 10 years on wolf x dog hybridization and related topics. Consensus was reached that admixed individuals should always be defined according to their genetic profile, and that a reference threshold for admixture (i.e., q-value in assignment tests) should be formally adopted for their identification. To mitigate hybridization, experts agreed on adopting preventive, proactive and, when concerning small and recovering wolf populations, reactive interventions. Overall, experts' consensus waned as the issues addressed became increasingly practical, including the adoption of lethal removal. We suggest three non-mutually exclusive explanations for this trend: (i) value-laden viewpoints increasingly emerge when addressing practical issues, and are particularly diverging between experts with different disciplinary backgrounds (e.g., ecologists, geneticists); (ii) some experts prefer avoiding the risk of potentially giving carte blanche to wolf opponents to (illegally) remove wolves, based on the wolf x dog hybridization issue; (iii) room for subjective interpretation and opinions result from the paucity of data on the effectiveness of different management interventions. These results have management implications and reveal gaps in the knowledge on a wide spectrum of issues related not only to the management of anthropogenic hybridization, but also to the role of ethical values and real-world management concerns in the scientific debate.

Microdosing and Other Phase 0 Clinical Trials: Facilitating Translation in Drug Development

Abstract: A number of drivers and developments suggest that microdosing and other phase 0 applications will experience increased utilization in the near-to-medium future. Increasing costs of drug development and ethical concerns about the risks of exposing humans and animals to novel chemical entities are important drivers in favor of these approaches, and can be expected only to increase in their relevance. An increasing body of research supports the validity of extrapolation from the limited drug exposure of phase 0 approaches to the full, therapeutic exposure, with modeling and simulations capable of extrapolating even non-linear scenarios. An increasing number of applications and design options demonstrate the versatility and flexibility these approaches offer to drug developers including the study of PK, bioavailability, DDI, and mechanistic PD effects. PET microdosing allows study of target localization, PK and receptor binding and occupancy, while Intra-Target Microdosing (ITM) allows study of local therapeutic-level acute PD coupled with systemic microdose-level exposure. Applications in vulnerable populations and extreme environments are attractive due to the unique risks of pharmacotherapy and increasing unmet healthcare needs. All phase 0 approaches depend on the validity of extrapolation from the limited-exposure scenario to the full exposure of therapeutic intent, but in the final analysis the potential for controlled human data to reduce uncertainty about drug properties is bound to be a valuable addition to the drug development process.

Research productivity and academics’ conceptions of research

Abstract: This paper asks the question: do people with different levels of research productivity and identification as a researcher think of research differently? It discusses a study that differentiated levels of research productivity among English and Australian academics working in research-intensive environments in three broad discipline areas: science, engineering and technology; social science and humanities; and medicine and health sciences. The paper explores the different conceptions of research held by these academics in terms of their levels of research productivity, their levels of research training, whether they considered themselves an active researcher and a member of a research team, and their disciplinary differences.

A Deep Learning Approach to Anomaly Detection in Nuclear Reactors

Abstract: In this work, a novel deep learning approach to unfold nuclear power reactor signals is proposed. It includes a combination of convolutional neural networks (CNN), denoising autoencoders (DAE) and $k$-means clustering of representations. Monitoring nuclear reactors while running at nominal conditions is critical. Based on analysis of the core reactor neutron flux, it is possible to derive useful information for building fault/anomaly detection systems. By leveraging signal and image pre-processing techniques, the high and low energy spectra of the signals were appropriated into a compatible format for CNN training. Firstly, a CNN was employed to unfold the signal into either twelve or forty-eight perturbation location sources, followed by a $k$-means clustering and $k$-Nearest Neighbour coarse-to-fine procedure, which significantly increases the unfolding resolution. Secondly, a DAE was utilised to denoise and reconstruct power reactor signals at varying levels of noise and/or corruption. The reconstructed signals were evaluated w.r.t. their original counter parts, by way of normalised cross correlation and unfolding metrics. The results illustrate that the origin of perturbations can be localised with high accuracy, despite limited training data and obscured$/$noisy signals, across various levels of granularity.