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The quality, accuracy and appropriateness of UK optometric age-related macular degeneration referrals
No full textYesPurpose
Little is known about the quality of optometrists' referrals to secondary care for neovascular age-related macular degeneration (nAMD), despite the need for timely intervention. We analysed the content and accuracy of optometrists' referrals for nAMD. Adherence to UK National Institute for Health and Care Excellence (NICE) guidelines and the impact of the COVID-19 pandemic were assessed as secondary measures.
Methods
Optometric referrals to a specialist macular treatment centre in Bradford, United Kingdom, between March 2019 and March 2021 were retrospectively analysed and compared with subsequent electronic medical records. Data were extracted on legibility, reason for referral, patient and optometrist demographics, visual acuity, reported signs and symptoms, patient diagnosis and patient outcomes. Binomial logistic regression models were constructed to determine whether signs or symptoms noted in the referral were associated with subsequent nAMD diagnosis in secondary care and whether optometrist gender or experience influenced nAMD referral accuracy.
Results
Across all 394 referrals analysed, 256 were for nAMD. Referral accuracy for nAMD was 39.8% (95% CI [34.0%, 45.9%]), with the most common reason for misdiagnosis being dry AMD. However, 76.8% of patients referred for suspected nAMD were either treated in secondary care or observed over multiple visits. 20% of suspected nAMD patients were seen within the NICE recommended 14-day window pre-COVID, dropping to 5% during the pandemic (p < 0.001). Visual acuity was most strongly associated with nAMD diagnosis (χ2(1) = 13.71, p < 0.001) followed by macular haemorrhage (χ2(1) = 5.89, p = 0.02). Neither optometrist gender nor experience was significantly associated with confirmed nAMD. Legibility of referrals was 91–95% for patient details and 94–97% for the referring optometrist.
Conclusions
Although the overall quality and legibility of optometrists' macular referrals to secondary care were of a high standard, the diagnostic accuracy of nAMD was below 40%. Referred visual acuity was the main sign/symptom associated with confirmed nAMD diagnosis
Bear factor and hedge fund performance
No full textYesWe find that hedge funds that have low (negative) return covariance with the return of a bear spread portfolio (i.e., Bear factor) after controlling for the market factor, earn significantly higher returns in the cross-section. The return spread does not reflect bear risk premia; instead, it represents a low risk-high return relation. We decompose the Bear factor into different components to identify the one driving the bear beta effect on fund performance and show that the return spread can be attributed to the differential ability of low bear beta funds to reduce their market exposures when the market declines and the VIX increases (i.e., downside timing). Further analysis suggests that these fund managers are more skilled at selling overpriced insurance during volatile market periods. Overall, we propose a simple option-implied predictor of hedge fund returns and unravel a novel economic mechanism that associates the Bear factor exposure with fund performance
Home–country technological legitimacy in crowdfunding: The moderating role of positive psychological capital language
No full textYesThis study uses the legitimacy theory as the theoretical lens to investigate how and under what circumstances home-country technological legitimacy affects the performance of crowdfunding campaigns in emerging markets. A dataset of 758 technology crowdfunding campaigns from Kickstarter was analysed. Our findings reveal that higher home-country technological legitimacy (measured by the Global Innovation Index) affects crowdfunding performance (measured by the ratio of funds raised to the pledged goal). We also find that positive psychological capital language (optimism, insistence, and tenacity) plays a crucial moderating role in strengthening this relationship. This study extends the legitimacy theory by demonstrating that country-level technological legitimacy is an external signal that shapes backer perceptions and funding decisions. Our results highlight the importance of campaign narratives in overcoming institutional voids and enhancing entrepreneurial success in global crowdfunding initiatives. These insights offer practical implications for entrepreneurs, policymakers, and investors seeking to navigate the complex dynamics of crowdfunding in emerging economies
Embedding a Data-Driven Decision-Making Work Culture in a Social Housing Environment
No full textYesThis paper explores the issue of delayed rent payments in social housing in the United Kingdom and its impact on tenants and housing providers. Our approach is to use machine learning algorithms to analyse payment patterns and identify tenants who may be at risk of falling behind on rent payments. By doing this, we aim to equip housing providers with the necessary tools to intervene early and maintain consistent tenancies. We have conducted research using machine learning models such as decision trees and random forests to address this issue. The paper emphasises the potential benefits of Explainable AI, which can help build trust in data-driven decision-making and AI among employees unfamiliar with AI and machine learning
Flexural performance of FRP-prestressed RPC beams reinforced with steel-FRP composite bars
No full textYesSix reactive powder concrete (RPC) beams, reinforced with non-prestressed steel-FRP composite bars (SFCBs) and prestressed Fiber Reinforced Polymer (FRP) bars, were fabricated to investigate their flexural performance. Four-point bending tests were conducted on beams with different types of concrete, prestressing bars and prestress levels. The test results demonstrated that prestressing significantly enhances the flexural performance of the beams tested. Compared with non-prestressed RPC beams, FRP-prestressed RPC beams exhibited a 24%, 20%, and 17% improvement in cracking, yield, and ultimate loads, respectively, along with an 81% increase in energy absorption capacity and reduced crack widths, but an 8% reduction in ductility. FRP-prestressed RPC beams significantly outperformed their ordinary concrete counterparts, with cracking, yield, and ultimate moments improved by 104%, 77%, and 58%, respectively. Increasing the pre-tension stress of FRP bars enhanced the cracking moment and reduced deflection. However, higher prestress level leads to lower bearing capacity and energy absorption due to premature rupture of prestressed reinforcements before crushing of compression concrete. The type of prestressing reinforcement, whether FRP or steel, has little effect on the cracking moment but significantly affected ductility and energy absorption. Based on simplified material constitutive models and rational fundamental assumptions, two bending failure modes were identified for FRP prestressed RPC beams. Discriminant criteria for these modes of failure were established, along with simplified formulas for predicting the bearing capacity of the beams. These findings provide a technical reference for the design and application of FRP-prestressed RPC beams reinforced with SFCBs.Natural Science Foundation of China (52378201), the High-End Foreign Experts Project of Ministry of Science and Technology, China (G2022014054L), the Jiangsu Construction System Science and Technology Project (2023ZD104, 2023ZD105), the Yangzhou City and University Science and Technology Cooperation Fund Project (YZ2022194), the Yangzhou Construction System Science and Technology Project (202309, 202312), the Nantong Jianghai (226) talents project, the Research Project of Jiangsu Civil Engineering and Architecture Society (the Second Half of 2022), the Graduate Research and Innovation Projects of Jiangsu Province (KYCX24_3750), the Jiangsu Provincial Government Scholarship for Studying Abroad (2024), and the Excellent Doctoral Dissertation Fund of Yangzhou University (2024
Countering Islamophobia on Campus
No full textYesThis report is based on qualitative research conducted at the University of Bradford (UoB) during the Summer and Autumn of 2024. The project sought to explore the perceptions and experiences of Muslim staff and students in the Academy. The researchers undertook a total of 30 semi-structured interviews via Microsoft Teams with Muslim students (UG, PGT and PGR) and staff (academics and those occupying professional services roles) in order to address the following questions:
1. What are the perceptions and experiences of Muslim students and staff of studying/working in the Academy?
2. Do they feel a sense of belonging on campus and that their Muslim identities are adequately accommodated?
3. What is the efficacy of institutional interventions (policy, structures, EDI initiatives) for tackling prejudice and discrimination?
4. To what extent are current mechanisms for reporting instances of Islamophobia effective?
An important caveat to note is that whilst the study sample was based at the UoB at the time of the research, their perceptions and experiences were not always necessarily in relation to their time at UoB and, indeed, at times, related to their experiences at previous institutions as well as their general viewpoints of the sector overall. This research produced a range of findings, some of which are perhaps more unusual and distinctive than others. IThe Aziz Foundation and The University of Bradfor
Humanitarian Decision-making: A Multidisciplinary Approach towards Enhancing Quality and Well-being
No full textYesAim:
Establishing a systematic and practical framework for decision evaluation in humanitarian logistics.
Objectives:
Identify and compare existing theory and practice in humanitarian logistics decision making.
Measure determinants influencing decision making in humanitarian logistics.
Design and validate a framework for assessment, monitoring, and evaluation.
Contribution to standardized decision making and reducing subjectivit
Latent Semantic Analysis for Feature Selection: A Proposed Approach for Anomaly Detection in Network Traffic
No full textNoIn recent times, there has been a paradigm shift in technological advancement that has brought about a revolution in every aspect of our lives. Advancements in technologies such as the Internet of Things (IoT), cloud computing and emerging wireless connectivity are now an integral part of our lives and have become an essential national infrastructure component. However, with the advancements in these technologies, cyber attacks have become more sophisticated and stealthier, making it increasingly challenging to detect them using typical layers of defence such as firewalls a nd an tivirus so ftware, w h ich are predominantly rule-based. To complement these layers of defence, Intrusion Detection Systems (IDSs) have been developed. This work proposes an adaptive feature selection approach as part of IDSs. Its effectiveness in detecting anomalies in network traffic has been demonstrated, and it can complement traditional layers of defence such as firewalls and antivirus software. The prop osed approach involves a two-step process: first, pr eprocessing and feature selection, and second, the training and deployment of a Machine Learning (ML) model for the detection of anomalies in network traffic. The p roposed a pproach u tilises a n unsupervised learning technique called Latent Semantic Analysis (LSA) for feature selection. To evaluate the effectiveness of the proposed approach, a publicly available dataset is used, which yields an accuracy, True Positive Rate (TPR), and F-Score of 99.89%, 100%, and 99.94% respectively. Furthermore, the proposed approach yields a lowest False Positive Rate (FPR) of 0.68%
Flow pattern and turbulent kinetic energy analysis around tandem piers: insights from k-ε modelling and acoustic doppler velocimetry measurements
No full textYesThis study investigated the distribution and dynamics of the Turbulent Kinetic Energy (TKE) around a group of three tandem piers using a combination of numerical simulations and experimental measurements. The Volume of Fluid (VOF) method, coupled with the k-
turbulence model, was implemented in ANSYS FLUENT to replicate the free-surface flow conditions. An experimental validation was conducted using Acoustic Doppler Velocimetry (ADV) to assess the model’s capability at capturing the turbulence characteristics. While the model effectively reproduced the near-bed turbulence, it consistently underestimated the TKE magnitudes across the flow domain, particularly in regions of strong vortex-induced turbulence. Discrepancies emerged in the confined regions between the piers, where the velocity profiles were overestimated at the surface and underestimated near the bed and mid-depth, impacting the TKE predictions. Despite these inconsistencies, the general pattern of the TKE distribution aligned with the experimental trends, though the absolute values remained underestimated due to the inherent limitations of the k- model. The model’s performance in less turbulent regions demonstrated improved accuracy, reinforcing its applicability for moderate turbulence simulations. To further examine the interaction between vortex structures and the TKE, velocity distributions were analyzed at three specific depths (z/h = 0.15, 0.4, and 0.62). The findings showed the critical role of vortex shedding in TKE generation and dissipation, with notable variations in the turbulence intensity influenced by structural confinement effects. This study offers a novel, high-resolution evaluation of the k-ε model’s ability to predict TKE distributions around tandem piers, using spatially detailed comparisons with the experimental data. Unlike previous studies that broadly acknowledged the model’s limitations, this work systematically identifies the specific regions, particularly vortex-dominated zones, where its predictive accuracy significantly degrades
Anti-inflammatory, analgesic, and anxiolytic effects of crude extracts and isolated bioactive fractional compounds from Pouzolzia sanguinea
No full textYesPharmacological relevance: Ethnic people residing in the Chittagong Hill Tracts (CHTs) of Bangladesh use Pouzolzia sanguinea to alleviate flatulence, for menstruation, inflammation, insomnia, and analgesia. However, there is no scientific evidence regarding the bioactivity of these plants. Aim: This study aimed to isolate bioactive fractional compounds from Pouzolzia sanguinea (IFCPS) crude extract to assess the anti-inflammatory, analgesic, and anxiolytic activities. Materials and Methods: Preparative TLC-bioautography and silica gel two-stage column chromatography were used to isolate bioactive fractional com-pounds from P. sanguinea methanol crude extracts. The anti-inflammatory, analgesic, and anxiolytic activities of extracts and IFCPS were studied by inhibiting protein denaturation, acetic acid-induced writhing, Eddy’s hot plate, field cross, and hole cross methods. Results: The dried crude extract’s chemical analysis revealed alkaloids, flavonoids, terpenoids, saponins, vitamin C, and tannins. Nine single isolated fractional compounds (IFC1PS to IFC9PS) were isolated through TLC. Among these, IFC2PS exhibited (p˂0.01) the most potent anti-inflammatory activity in the inhibition of protein denaturation studies (70.51%), which was slightly lower than acetyl salicylic acid (82.29%), at160 µg/mL. This inhibitory effect occurred in a dose-dependent manner. IFC2PS exhibited the most potent peripheral analgesic and moderate central analgesic effects compared to the standard. In contrast, IFC1PS showed moderate effects in both areas. IFC8PS showed superior anxiolytic activities compared to crude extracts and other IFCPS. Conclusions: Out of the nine fractional compounds isolated, the IFC2PS reduced pain and inflammation, whilst IFC8PS exhibited anxiolytic activities. This is the first comprehensive study demonstrating the anti-inflammatory, analgesic, and anxiolytic effects of crude extracts and isolated fractional compounds from the whole plant of P. sanguinea, which may have immediate experimental and clinical applications