17628 research outputs found
Sort by
On the Eigenvalue Distribution of Spatio-Spectral Limiting Operators in Higher Dimensions, II
No full text[Abstract is missing mathematical symbols.]Let F, S be bounded measurable sets in . Let be the orthogonal projection on the subspace of functions with compact support on F, and let be the orthogonal projection on the subspace of functions with Fourier transforms having compact support on S. In this paper, we derive distributional estimates on the eigenvalue sequence of the spatio-spectral limiting operator. The significance of such estimates lies in their diverse applications in medical imaging, signal processing, geophysics and astronomy. For suitable domains F and S, we prove thatwhererepresents the Lebesgue measure of the domain, and the error term satisfies the following bound:whereand denote the -dimensional Hausdorff measures of the boundaries of F and S, while are geometric constants related to an Ahlfors regularity condition on the domain boundaries. When F and S are Euclidean balls, we expect this estimate to be sharp up to logarithmic factors. This improves on recent work of Marceca-Romero-Speckbacher (Arch. Rational Mech. Anal., 2024) which showed that for any . Our proof is based on the decomposition techniques developed by Marceca-Romero-Speckbacher. The novelty of our approach lies in the use of a two-stage dyadic decomposition with respect to both the spatial and frequency domains, and the application of results in the authors’ prior work on the eigenvalues of spatio-spectral limiting operators associated to cubical domains
Using citizen science photographs to identify reproductive events in an oviparous elasmobranch
Get PDFIdentifying critical habitats is important for the effective management of vulnerable species. Critical habitats, such as mating or nursery grounds, support populations during key life stages and help to maximise reproductive output and population growth. In elasmobranchs, mating often happens over a defined season, suggesting sites associated with this process may only require temporal protection. However, knowledge gaps on such sites exist for many elasmobranchs due to the challenges associated with identifying temporal mating periods, which hinders conservation efforts. Here, we investigated the application of photographs to estimate reproductive timing in an oviparous elasmobranch, the flapper skate (Dipturus intermedius), as a non‐invasive and low‐cost alternative to other approaches. Using a pre‐existing citizen science photo‐ID database of over 2000 images, we identified signs of reproductive behaviour: the presence or absence of pelvic swelling, bite wounds and scratch wounds. Statistical models were created for each feature to explore seasonal trends and other parameters explaining their presence. Seasonal trends were present for all features and feature occurrence differed with sex. The occurrence of bite wounds and pelvic swelling in flapper skate peaked over winter and spring months, suggesting a winter–spring mating and egg‐laying period. These results are corroborated by previous reproductive research on the flapper skate, suggesting the applied method is a valid tool to estimate reproductive timing in an elusive elasmobranch. The approach could be applied to other flapper skate populations and other elasmobranch species, helping to close existing knowledge gaps on reproductive behaviours
A Hybrid LATAM and Few-Shot Learning Framework for Fault Diagnosis in Wireless Sensor Networks
No full textWireless Sensor Networks (WSNs) are widely used for real-time monitoring in industrial automation, environmental sensing, and healthcare. However, sensor faults pose significant challenges, potentially leading to compromised data integrity and reduced system reliability. Traditional fault detection methods often rely on large labeled datasets, which are scarce in real-world scenarios. This paper proposes a novel data-efficient framework that integrates unsupervised learning for fault detection with few-shot learning for fault classification, addressing the challenge of a limited number of fault data in WSNs. For fault detection, we introduce the LSTM Autoencoder with a Temporal Attention Mechanism (LATAM) model, trained on normal data, that identifies anomalies based on reconstruction errors and a dynamic thresholding technique. For fault classification, we employ a few-shot learning approach using Convolutional Kernel Transform (MiniROCKET) for efficient feature extraction, and Prototypical Networks to classify faults with a minimal number of labeled samples. Due to the lack of comprehensive public fault datasets, the proposed framework was evaluated on a dataset combining real-world healthy temperature sensor readings with synthetically generated faults. Experimental results demonstrate that the LATAM model achieves high accuracy in fault detection, with an F-score of 98.90%. The few-shot classification approach performs effectively in low-data scenarios, achieving F-scores of 94.18% in a 15-shot setting and 84.75% in a one-shot setting. Compared to the traditional 1D CNN, LSTM, and Matching Networks methods, the proposed framework exhibits superior performance, particularly in data-scarce environments. By eliminating reliance on large labeled datasets and pre-trained models, this approach effectively addresses the challenges posed by data scarcity in WSNs
How Different Stakeholders Perceive Benefits, Challenges, and Barriers in the Implementation of Green Technology Projects
Get PDFDiffering stakeholder interests often lead to the application of varying criteria when evaluating green technology projects. This heterogeneity can impede project outcomes by making it challenging to reconcile conflicting perspectives. The present study empirically examines stakeholder alignment in relation to the perceived benefits and barriers to green technology implementation. Insights from a focus group comprising 15 project stakeholders were used to identify key barriers, which were subsequently ranked using survey data collected from 286 UAE-based stakeholders. A customised fuzzy-based Failure Mode and Effects Analysis tool (FMEA–FST) was applied to prioritise these factors. The results reveal significant variation in the salience of factors across stakeholder groups, highlighting both notable differences and shared framing biases. The study’s originality lies in its use of the bespoke FMEA–FST model to prioritise factors, thereby identifying the relative importance of benefits, barriers, and challenges. Notably, ‘Lack of support from senior management’ emerged as the most critical factor across all categories, while ‘Potentially lower benefits for small or less complex projects’ was deemed the least important. To foster greater stakeholder alignment, the study recommends strengthening social relationships to bridge divergent perspectives. Limitations include the inability to account for changes in factor salience across different stages of the project lifecycle, as well as the exclusion of temporal and typological effects. These limitations present opportunities for future research
Integrated simulation and optimisation framework for techno-economic evaluation of BIPV systems: A Scottish case study
Get PDFBuilding Integrated Photovoltaics (BIPV) offers an innovative approach to achieving net-zero energy buildings by seamlessly integrating solar energy systems into architectural designs. However, the widespread adoption of BIPV systems is often hindered by concerns over high initial costs and uncertain economic returns. This study conducts a comprehensive techno-economic assessment of BIPV systems in Scotland to evaluate their energy generation potential, cost-effectiveness, and financial viability across different urban locations. A 1 kWp grid-connected BIPV system was simulated using PVsyst software for four distinct urban areas in Scotland—Aberdeen, Glasgow, Edinburgh, and Portree. The analysis considered 10 different tilt angles (0° to 90° in 10° increments) and 8 azimuth angles (-180° to 180° in 45° increments) to cover a wide range of building orientations. Key performance indicators, including Annual Energy Generation (AEG), Levelized Cost of Energy (LCOE), and Payback Period (PP), were evaluated to determine the feasibility of BIPV systems in these locations. Additionally, MATLAB was used solely to generate 3D graphs for clearer visualisation and more effective analysis, utilising the data obtained from PVsyst simulations. The findings reveal that Aberdeen provides the most favourable conditions for BIPV deployment, achieving an AEG of 1167 kWh/year, an LCOE of 0.0606 GBP/kWh, and a PP of 7.2 years at an optimal orientation of 0° azimuth and 40° tilt. Conversely, Portree exhibited the least favourable outcomes, with an AEG of 312 kWh/year, an LCOE of 0.2268 GBP/kWh, and a PP of 17.4 years at 180° azimuth and 90° tilt. These results offer valuable insights for policymakers, solar developers, and building designers, helping optimise Scotland's BIPV deployment strategies for enhanced economic and environmental benefits
Investigating MICP sand stabilization with Bacillus thuringiensis: nutrient concentration and availability
Get PDFThis study investigates Bacillus thuringiensis TISTR 126 in Microbially Induced Calcium Carbonate Precipitation (MICP) for sand stabilization in neutral pH environments. It addresses a gap in research on non-alkali-tolerant bacteria. Most MICP studies focus on ureolytic bacteria, which thrive in alkaline conditions. B. thuringiensis is cost-effective, widely available, non-pathogenic, and suitable for neutral pH, making it a promising alternative. The study investigates the effects of nutrient availability and calcium chloride (CaCl2) on calcium carbonate formation and the mechanical properties of sand, including water permeability, unconfined compressive strength (UCS), and internal friction angle. Results show that optimizing the cementation solution enhances sand properties, leading to a 12.1% increase in density, a ten-fold reduction in water permeability, and a UCS of approximately 339.6 kPa. The highest cementation ratio produced an internal friction angle of 48°, indicating a dense structure. This research addresses the critical gap in nutrient optimization for MICP processes. It introduces B. thuringiensis as a viable, sustainable, and non-pathogenic alternative to traditional ureolytic bacteria for sand stabilization, broadening the scope of MICP applications
Multi-objective optimisation for energy-centric offloading in fog computing
No full textThe increasing demand for real-time and energy-efficient task execution in Internet of Things (IoT) applications has positioned fog computing as a latency-sensitive alternative to traditional cloud-based processing. However, existing task-offloading strategies often neglect the multidimensional aspects of energy consumption—particularly parameters such as memory usage and data transfer—and rely on static heuristics that struggle in dynamic, heterogeneous environments. To address these limitations, we propose a new multi-objective energy-centric task offloading algorithm that integrates ECO-PSO and ECO-CSA with a dynamic coefficient adjustment mechanism based on gradient ascent. Our proposed solution explicitly models energy consumption in computing, transmission, and memory, along with makespan, enabling fine-grained task distribution among heterogeneous fog nodes. Furthermore, a customised energy model is introduced to select appropriate nodes with low CPU utilisation, enhancing energy efficiency and system performance. Numerous simulations conducted using the LEAF simulator in realistic fog environments demonstrate that our proposed method consistently outperforms PSG, PSG-M, CCFO, and MoAOA algorithms, achieving up to 25% lower total energy consumption and up to 30% reduction in makespan, while ensuring a more balanced task distribution. Therefore, the proposed algorithm is a valuable solution for creating future-oriented IoT systems in resource-constrained fog environments
Habitat selection by nightjars in Ashdown Forest in 2023
Get PDFThe Nightjar (Caprimulgus europaeus), a nocturnal, insectivorous bird that migrates to the UK for breeding during summer months, exemplifies the urgent need for such habitat-focused research. This cryptic species has experienced significant population declines across its range, leading to its classification as an amber listed species under the Birds of Conservation Concern 5. This study aimed to inform our understanding of Nightjars at Ashdown Forest, a 2500 ha ancient expanse of open heathland located within the High Weald Area of Outstanding Natural Beauty in East Sussex. Volunteer survey data were used to (i) quantify habitat composition within the study area, within home ranges, and within song territories, and (ii) use these data to examine patterns in Nightjar habitat selection at two spatial scales in relation to various environmental factors. Ultimately, the aim was to determine the importance of each scale in habitat selection to inform habitat management at the site for Nightjar conservation. Nightjars in Ashdown Forest exhibited a degree of tolerance towards disturbance caused by human activities within the forest but notably avoided the most frequented areas. Overall, Nightjars relied on a mosaic of heathland and woodland features that provided both nesting cover and accessible prey. Heather grassland, woodland edges, and early successional habitats were key components. Avoidance of improved grassland, urban areas, road and footpath dense regions further highlights the importance of maintaining sufficient areas of suitable habitats away from key sources of disturbance. Continued monitoring will be essential to assess population trends and the effectiveness of conservation practices
Wild Encounters, Worn Ecosystems: Challenges and Lessons from Yala National Park
No full textYala National Park (YNP), Sri Lanka’s most visited protected area, is celebrated for its biodiversity and high leopard density. However, rapid tourism growth has contributed to habitat degradation, overcrowding and challenges in enforcing park regulations such as speed limits, route restrictions, and wildlife viewing guidelines which together weaken its ecological resilience. This case study explores these tensions through interviews with park administrators, highlighting how current management strategies attempt to balance rising tourism demand with conservation goals. Jeep safaris – the park’s flagship attraction – generate essential revenue but also cause congestion, alter wildlife behaviour, and intensify ecological strain. Measures such as speed limits and rest periods for vehicles have seen only partial success due to limited enforcement capacity and institutional constraints. Community-based livelihood initiatives, including bee box projects and aloe vera farming, aim to reduce human-wildlife conflict but remain economically vulnerable without stable market access or long-term institutional support. Additionally, inconsistent reinvestment of tourism revenue into park conservation, coupled with political interference, further undermines regulation and strategic planning. These findings point to the urgent need for institutional reform, transparent revenue allocation, and deeper integration of community engagement into conservation planning
Data sources:
Access Repository Dashboard
Do you manage Repository@Napier? Access insider analytics, issue reports and manage access to outputs from your repository in the CORE Repository Dashboard!