University of the West of Scotland
Research Repository and Portal - University of the West of ScotlandNot a member yet
10867 research outputs found
Sort by
Ethics and Education special issue - hope, agency, and the question of the future: education’s role in responding to the climate emergency
Full text linkThis paper proposes ideas of an ethics that destabilizes the axis of anthropocentrism around which climate change education revolves. Education is a future-oriented practice, narrated as aiming at ‘solving’ the climate crisis, or ‘saving’ the planet. I set out to unsettle established notions of humans’ ethical responsibility for the world, considering instead our capacity to be responsible with non-human others. What follows is an ecological ethics that conceives of humanity as mutually interdependent and inextricably involved in complex interrelationships with plants. Plants are seen as being beyond ethical considerations; reduced to the level of resources, food sources, or ornaments, we do not feel the need to question or consider our ethical responsibilities to them. We begin by travelling the greatest distance from ourselves, the paper proposes how we might begin this ethics, centered on the notion of being responsible as remaining open and attentive to the other, and answering – through meaningful action – in reply. The conversational responsiveness of this ethics does not assume mastery over plants, nor does it seek to reduce plants to images of ourselves. Indeed, this new understanding of responsibility presupposes the agency and awareness of plants
Toward holistic COPD management:the case for mental health integration
Full text linkBackground and Aims Chronic obstructive pulmonary disease (COPD) is a growing global public health concern, not only due to its physical effects but also because of the significant psychological distress it causes, including anxiety and depression. This perspective stresses the importance of addressing mental health issues in the management of COPD, discussing current treatment options, which include non-pharmacological interventions.Methods This perspective synthesizes current literature on psychological distress in COPD and reviews evidence for non-pharmacological approaches, including pulmonary rehabilitation, cognitive behavioral therapy, self-management programs, telerehabilitation, education, and peer support. It draws on recent literature and guidelines to identify gaps and opportunities for integrated care.Results Individuals with COPD experience substantially higher rates of anxiety and depression compared to the general population, and this can negatively impact quality of life, disease progression, and healthcare outcomes. Despite this, mental health symptoms often remain undiagnosed and untreated due to limited awareness, training, and resources. Psychological and non-pharmacological interventions reveal encouraging results in reducing distress and improving overall well-being. Pulmonary rehabilitation, combined with psychological support, demonstrates particular benefits but is underutilized due to patient and systemic barriers. Alternative approaches such as telerehabilitation and remote therapies offer potential for increased access. Moreover, education and peer support play a crucial role in empowering patients, improving coping skills, and fostering social connectedness, which contribute positively to psychological well-being. This perspective advocates for integrated COPD management, which prioritizes mental health literacy, collaborative care models, and patient engagement.Conclusion Addressing both the physical and psychological aspects of COPD is essential for holistic care and enhancing the quality of life of individuals with COPD. Further research and healthcare policy efforts are needed to close existing gaps and deliver comprehensive support for people living with COPD
A secure routing protocol for underwater acoustic sensor networks using reinforcement learning
Full text linkUnderwater acoustic sensor networks are essential for underwater environment surveillance and monitoring and offshore exploration. Underwater acoustic sensor network experience challenges because of the hostile underwater environment, including bandwidth limitation, node mobility, propagation high propagation delays and security threats. Reinforcement Learning (RL) is a branch of machine learning where an agent learns to make decisions by interacting with an environment and receiving feedback in the form of rewards or penalties. The importance of reinforcement learning lies in its ability to handle complex decision-making problems where explicit supervision is difficult or impossible. This paper proposes a novel Reinforcement Learning-based Secured Routing Protocol (RL-SRP) for underwater acoustic sensor network. The proposed protocol integrates Q-learning with a trust management system to dynamically select secure and energy efficient routes while mitigating common attacks which consist of blackhole attack. Simulation results indicates that RL-SRP significantly improves packet delivery ratio, reduces end-to-end delay, and enhances network security and energy efficiency compared to existing routing protocols DBR and AODV
Exploring the impact of digital entrepreneurship on supply chain resilience in a post-pandemic context
Full text linkThis study explores the role of digital entrepreneurship in enhancing supply chain resilience in a post-pandemic context, focusing on the Nigerian landscape. This chapter examines how digital technologies such as blockchain, artificial intelligence (AI), and the Internet of Things (IoT) mitigate supply chain disruptions while fostering agility and adaptability in supply chain operations. The research addresses the theoretical gap in exploring the commitment to digital entrepreneurship and its impact on supply chain resilience, especially in resource-constrained environments like Nigeria. The findings emphasise the transformative potential of digital entrepreneurship in addressing systemic supply chain challenges in Nigeria, offering strategic insights for business leaders and policymakers. The research highlights the urgent need for investment in digital infrastructure to ensure sustainable and competitive supply chain ecosystems.<br/
Native trees are related to advanced bird breeding phenology and increased reproductive success along an urban gradient
Full text linkUrban areas are altered from natural landscapes in several ways that can impact wildlife. Birds are widespread in urban areas, and it is well documented that there are phenotypic differences between urban and non-urban conspecifics. However, little is known about which characteristics of the urban environment are driving differences. We used 9 years of data from nest boxes spread across 20 sites along a 40-km urban–non-urban gradient in Scotland to test whether characteristics of the urban environment (native, non-native, native oak (Quercus spp.), birch (Betula spp.) foliage availability, temperature and human population density, and the interaction between foliage and temperature) influenced phenology and reproductive success in blue tits (Cyanistes caeruleus). We found that higher foliage availability of native foliage, and specifically of the most common native genus, oak, was associated at the territory level with earlier first egg laying date. Higher non-native foliage availability at both a site and territory level was negatively related to clutch size. The number of fledglings produced was reduced at sites with higher levels of non-native foliage and increased at sites with greater amounts of native oak foliage present. We also found territories with a higher human population density had reduced fledging success. Temperature was negatively related to first egg laying date, clutch size and the number of fledglings produced. Moreover, the number of Lepidopteran larvae, blue tits' preferred prey, that were collected over the breeding season was positively related to native oak foliage availability. Our results strongly indicate that the presence of native trees, such as oak, are beneficial to breeding insectivores by increasing the number of fledglings they can successfully raise, likely due to the increased availability of invertebrate prey. We suggest that urban planting regimes should be carefully considered, selecting tree species that are native or non-native congeneric species, and most importantly that will host Lepidoptera larvae. This will not only help to support complete food chains, but also to maximize biodiversity and ecosystem services of urban green spaces
Black hole spectroscopy and tests of general relativity with GW250114
Full text linkThe binary black hole signal GW250114, the loudest gravitational wave detected to date, offers a unique opportunity to test Einstein’s general relativity (GR) in the high-velocity, strong-gravity regime and probe whether the remnant conforms to the Kerr metric. Upon perturbation, black holes emit a spectrum of damped sinusoids with specific, complex frequencies. Our analysis of the postmerger signal shows that at least two quasinormal modes are required to explain the data, with the most damped remaining statistically significant for about one cycle. We probe the remnant’s Kerr nature by constraining the spectroscopic pattern of the dominant quadrupolar (ℓ = =2) mode and its first overtone to match the Kerr prediction to tens of percent at multiple postpeak times. The measured mode amplitudes and phases agree with a numerical-relativity simulation having parameters close to GW250114. By fitting a parametrized waveform that incorporates the full inspiral-merger-ringdown sequence, we constrain the fundamental (ℓ = =4) mode to tens of percent and bound the quadrupolar frequency to within a few percent of the GR prediction. We perform a suite of tests—spanning inspiral, merger, and ringdown—finding constraints that are comparable to, and in some cases 2–3 times more stringent than those obtained by combining dozens of events in the fourth Gravitational-Wave Transient Catalog. These results constitute the most stringent single-event verification of GR and the Kerr nature of black holes to date, and outline the power of black-hole spectroscopy for future gravitational-wave observations
Nature-based Solutions (NbS) in agricultural soils for greenhouse gas mitigation
Full text linkGreenhouse gases (GHG), accumulated in the atmosphere, are the main cause of climate change. In 2017, the increase in average temperature was about 1 °C (between 0.8 °C–1.2 °C) above pre-industrial levels. Global warming refers to the increase in air surface, sea surface, and soil surface temperature and according to IPCC (Intergovernmental Panel Climate Change), since the industrial revolution, C emissions are due to land use changes like deforestation, biomass burning, conversion of natural lands, drainage of wetlands, soil cultivation, and tillage. As the world population has increased, world food production has risen too with a subsequent increase in GHG emissions and agricultural production, which is worsened by climate change. Negative consequences are well known such as the loss in water availability and in soil fertility, and pest infestations which are climate change’s effects on agriculture activity. Climate change’s main aftermath is the frequency of extreme weather events influencing crop yields. As climate change exacerbates degradation processes, land management can mitigate its impact and aid adaptation strategies for climate change. About 21–37% of GHGs have been caused by the agriculture activity, so the application of Nature-based Solutions (NbS) like sustainable agriculture could be a way to reduce GHGs worldwide. The aim of this article is to review how NbS may mitigate GHG emissions from soil, with solutions defined as an integrated approach to tackle climate change and to sustainably restore and manage ecosystems, delivering multiple benefits. NbS is a low-cost tool working within and with nature, which holds many benefits for people and the environment
A scalable swarm intelligence algorithm for autonomous UAV search and rescue operations
Full text linkThis paper presents the design and implementation of an autonomous UAV-based search and rescue system developed within the Horizon Europe project P2CODE. The proposed system leverages a modular and scalable architecture integrating edge-based real-time video processing, AI-based human detection, asynchronous message communication, and persistent state logging, all orchestrated through a web-based operator interface. Central to the system is a swarm intelligence algorithm that partitions the search area among multiple UAVs, taking into account factors such as battery levels and initial positions to generate balanced and coherent flight paths. By combining a Divide Areas based on Robots’ initial Positions (DARP) method with a Spanning Tree Coverage (STC) algorithm, the system ensures efficient and complete coverage of large outdoor regions. The operational workflow supports both fully autonomous exploration and reactive human-in-the-loop intervention in response to real-time detections. This work contributes a practical blueprint for large-scale, multi-agent coordination in dynamic and unstructured environments, advancing the state of the art in autonomous search and rescue missions
A digital platform with activity tracking for energy management support in long COVID:a randomised controlled trial
Full text linkIn a 6-month pragmatic randomised controlled trial (RCT; ISRCTN16033549), we compared a just-in-time intervention to support energy management in adults with long COVID (LC) to standard care. Participants received either the ‘Pace Me’ app and a wearable activity tracker (intervention) or an app only with data entry screens (control). The intervention group received just-in-time messages on energy management when they reached 50%, 75%, and 100% of their daily ‘activity allowance’. The primary outcome was post-exertional malaise (PEM) measured by the DePaul Symptom Questionnaire-PEM (DSQ-PEM).Of 369 participants assessed for eligibility, 250 participants were randomised 1:1, and 77 controls and 84 intervention participants were included in the final per-protocol analysis. There was no time by group interaction for the DSQ-PEM. The intervention group value was 48 (95% CI 44-53) at baseline and 46 (95% CI 41-51) post-intervention (arbitrary units). The control group value was 47 (95% CI 42-52) at baseline and 44 (95% CI 39-49) at follow-up (interaction effect p=0.614, η²p=0.002; trivial). No individual question exhibited an interaction effect (p>0.05).Although the intervention had minimal effect compared to control, the substantial recovery rates previously reported in LC, coupled with our wide inclusion criteria may have masked intervention effects. Therefore, future studies should consider this energy management framework in conditions without such recovery rates, such as CFS
SSCATeR:sparse scatter-based convolution algorithm with temporal data recycling for real-time 3D object detection in LiDAR point clouds
Full text linkThis work leverages the continuous sweeping motion of LiDAR scanning to concentrate object detection efforts on specific regions that receive a change in point data from one frame to another. We achieve this by using a sliding time window with short strides and consider the temporal dimension by storing convolution results between passes. This allows us to ignore unchanged regions, significantly reducing the number of convolution operations per forward pass without sacrificing accuracy. This data reuse scheme introduces extreme sparsity to detection data. To exploit this sparsity, we extend our previous work on scatter-based convolutions to allow for data reuse, and as such propose Sparse Scatter-Based Convolution Algorithm with Temporal Data Recycling (SSCATeR). This operation treats incoming LiDAR data as a continuous stream and acts only on the changing parts of the point cloud. By doing so, we achieve the same results with as much as a 6.61-fold reduction in processing time. Our test results show that the feature maps output by our method are identical to those produced by traditional sparse convolution techniques, whilst greatly increasing the computational efficiency of the network