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Decoupling the Effects of Temperature, Strain, and Refractive Index in Long-Period Fiber Grating Used for Epoxy Resin Cure Monitoring
Get PDFEpoxy resins are widely used in the manufacture of composite materials for a wide range of applications. Control of the curing process is an important consideration in ensuring product quality and minimizing production times. The curing of epoxy resin is associated with temperature, strain, and refractive index changes but it is difficult to monitor these quantities individually and hence difficult to achieve accurate control of the curing process. One promising approach for monitoring these quantities is the use of long-period fiber gratings (LPFG). We analyze the spectral response of a LPFG in epoxy resins to temperature, strain, and refractive index. Wavelength shifts and dip amplitudes of cladding mode notches are monitored and are used to decouple temperature, strain, and refractive index for gratings in air, liquid, and hardened resins. The three measurands are found from wavelength shifts and dip amplitudes, employing multiplication by a weighted pseudo-inverse matrix assuming linear dependences between the spectral and external parameters. We propose a new model to describe the influence of fiber parameters and external refractive index, temperature, and strain on the spectral behavior of long-period fiber gratings in epoxy resins during hardening. The results obtained can be utilized for multiparameter cure process monitoring of epoxy resins by using long-period fiber gratings
Optimising digital advance care planning implementation in palliative and end-of-life care: a multi-phase mixed-methods national research programme and recommendations
Get PDFBackground : Digital advance care planning (DACP) is increasingly used globally for patients with life-limiting conditions to support real-time documentation and the sharing of preferences for care. There has been low engagement with DACP systems, with patients often having information about their care preferences documented late in their illness trajectory or not at all. To optimise implementation, the Optimal Care research programme sought to understand DACP system use from multiple perspectives to guide their development and evaluation. Methods: Between 2020 and 2023, our mixed-methods research programme sought an understanding of DACP implementation from multiple perspectives, including (i) national online survey of end-of-life care commissioning leads in England; (ii) online survey of community and hospital-based health and care professionals in two geographical regions; (iii) semi-structured interviews with a sample of survey respondents; (iv) focus groups and interviews with patients with life-limiting illness and their carers and (v) regional and national Theory of Change workshops. Findings were organised by five phases of a conceptual model of DACP generated during the programme and further categorised using the Non-adoption, Abandonment, Scale-up, Spread and Sustainability (NASSS) framework. Results: A total of 788 stakeholders participated. Twenty evidence-based recommendations were distilled from data collected across the research programme to guide the implementation of DACP in routine care. Considerations are provided across the five phases of DACP implementation (system design, recognition of clinical need for DACP, documentation processes, health and care professional engagement with DACP and DACP evaluation). Recommendations prioritise a focus on end-user needs and experiences, alongside highlighting the requisite need for DACP systems to support information exchange across settings involved in the care of people with life-limiting conditions. Conclusions: As currently designed and implemented, DACP systems may be falling short of their potential and are not working as intended for patients, carers and health and care professionals. The application of the recommendations should ensure consideration of the wider ecosystem in which DACP is being implemented, prioritising end-user experiences. Future research should prioritise developing approaches that target health and care professional DACP system engagement, alongside developing and evaluating patient and carer access to DACP systems
Kappa and Mu Opioid Receptors in Chronic Cough: Current Evidence and Future Treatment
Get PDFChronic cough is a significant burden on patient quality of life and is associated with poor health outcomes. Chronic cough may be a result of neural hypersensitivity due to changes in both the peripheral and the central nervous systems, although the exact mechanisms underlying its pathogenesis are not completely understood. Opioid receptors, specifically kappa and mu, are potential therapeutic targets in the management of chronic cough because they play a pivotal role in both the peripheral and the central neural pathways implicated in the act of coughing. Morphine, a mu opioid receptor agonist, is an effective cough modulator; however, mu receptor agonists are part of a drug class that can induce respiratory depression and euphoria, with strong reinforcing properties that may lead to excessive use and abuse. Drugs with a dual-acting mechanism of kappa receptor agonism and mu receptor antagonism may be effective in the management of chronic cough without the potential for abuse. This review summarizes the current understanding of the mechanisms of cough hypersensitivity, the role of the kappa and mu receptors in the neurophysiology of cough, and the clinical potential of targeting these receptors as a novel way of managing chronic cough
A comprehensive survey for Hyperspectral Image Classification: The evolution from conventional to transformers and Mamba models
No full textHyperspectral Image Classification (HSIC) presents significant challenges owing to the high dimensionality and intricate nature of Hyperspectral data. While traditional Machine Learning (TML) approaches have demonstrated effectiveness, they often encounter substantial obstacles in real-world applications, including the variability of optimal feature sets, subjectivity in human-driven design, inherent biases, and methodological limitations. Specifically, TML suffers from the curse of dimensionality, difficulties in feature selection and extraction, insufficient consideration of spatial information, limited robustness against noise, scalability issues, and inadequate adaptability to complex data distributions. In recent years, Deep Learning (DL) techniques have emerged as robust solutions to address these challenges. This survey offers a comprehensive overview of current trends and future prospects in HSIC, emphasizing advancements from DL models to the increasing adoption of Transformer and Mamba Model architectures. We systematically review key concepts, methodologies, and state-of-the-art approaches in DL for HSIC. Furthermore, we investigate the potential of Transformer-based models and the Mamba Model in HSIC, detailing their advantages and challenges. Emerging trends in HSIC are explored, including in-depth discussions on Explainable AI and interpretability concepts, alongside Diffusion Models for denoising, feature extraction, and fusion. Comprehensive experimental results were conducted on three Hyperspectral datasets to substantiate the efficacy of various conventional DL models. Additionally, we identify several open challenges and pertinent research questions in the field of HSIC. Finally, we outline future research directions and potential applications aimed at enhancing the accuracy and efficiency of HSIC. The Source code is available at https://github.com/mahmad000/HSIC-2024
Dextromethorphan versus Dextrorphan: A Quantitative Comparison of Antitussive Potency following Separate Administration of Metabolite
Get PDFTo assess the antitussive effects of dextrorphan (DOR) relative to its parent compound, dextromethorphan (DEX) a double-blind, randomized, placebo-controlled crossover study was conducted in 23 healthy volunteers using citric acid cough challenge test after administering placebo, DEX, or DOR. Plasma concentrations and cough frequency were monitored over 24 h, followed by model independent analysis and pharmacokinetic-pharmacodynamic (PKPD) modelling to discern the relative potency of each moiety. Model-independent pairwise analysis of the area under the effect curve (AUEC₀₋₂₄ h) showed no significant difference between DOR, DEX, and placebo's antitussive effects (p >.06), indicating the influence of considerable inter-individual variability and the need for larger sample sizes. The model-based analysis established DOR's relative potency at 26% compared to DEX, with maximum cough inhibition of 23% and IC50 of 0.3 ng/mL. PKPD measures were more accurate for DEX than DOR, particularly at lower baseline cough counts. In conclusion, while DOR retains some antitussive potency, since it is substantially less potent than DEX, higher relative concentrations are required to reach the same effect. Although separate administration of metabolite on its own is considered gold standard to establish its relative potency compared to parent compound, the variability in effect may prevent clear demonstration of effects without modelling particularly when these take benefit of the perturbing the balance of parent/metabolite ratios (e.g. via inhibition) or using the natural variational of such ratios in different individuals
Enhancing preceptorship to mitigate transition shock among newly registered nurses working in primary care
Get PDFTransition shock is a relatively new concept that describes the challenges of moving from the familiar role of preregistration nursing student to that of registered nurse. Newly registered nurses have to assume a new professional identity and meet the expectations that go with their new role while developing their skills and confidence for clinical practice. Transitioning to the role of general practice nurse can be particularly daunting due to the many pressures and the level of autonomous practice required in that setting. Preceptorship has a pivotal role in smoothing the transition, and preceptors can mitigate transition shock for newly registered nurses by offering support, empathy, encouragement and guidance. However, comprehensive and robust preceptorship programmes are not always offered in primary care settings and preceptorship in primary care needs to be enhanced
Towards an ecological systems approach to doctoral student resilience: qualitative evidence from the Covid-19 pandemic
Get PDFPurposeThis study aims to contribute to the growing body of literature documenting responses to short- and long-term impacts of the COVID-19 pandemic on doctoral students. This study examines support practices at different levels of the education system in which doctoral students are embedded, drawing on Bronfenbrenner’s ecological systems model to better understand how these contribute to doctoral students’ degree of resilience under stress.Design/methodology/approachUsing online group interviews, this study explores the experiences of 21 doctoral students from seven universities across Europe, Africa and Asia.FindingsThe analysis revealed that the quality of supervisor support at the microsystem level was the most crucial factor determining how severely the doctoral students experienced negative impacts from the pandemic. However, broader institutional and systemic challenges – including inadequate online infrastructure and lack of incentives for additional mentoring – limited the support options available to students. In settings with fewer institutional resources, students exhibited adaptive resilience by actively seeking alternative sources of support at the mesosystem level, particularly through peer networks and external mentors.Originality/valueThis study extends the literature on resilience in higher education settings. This study applies Bronfenbrenner’s ecological systems model to understand doctoral students’ experiences during the COVID-19 pandemic. It illustrates how the model can help understand the sources of individual resilience that are facilitated at different levels of the support systems. This study uses a sample of doctoral students with diverse characteristics in personal situations. Based on the findings, the study provides policy recommendations and identifies venues for further research needed in the field to understand the longer-term impact of the pandemic across different regional settings
Creating a low carbon, environmentally sustainable and socially just value chain for rare earth magnets. Final Report, Project 1008431
Get PDFFirst paragraph:The Project aimed to explore the potential effectiveness of socio-economic extended input-output modelling and to explore the possible overall impacts of potential interventions across the rare earth magnet value chain to decrease negative and increase positive economic impact whilst measuring the overall impact without focusing only on a single measure such as carbon reduction or job creation
An Adaptive Flooding Detection Framework with Blockchain Mitigation for Satellite Communications †
Get PDFSatellite communication has gained significant attention in the context of sixth-generation (6G) internet technology. Due to their high altitudes, dynamic link switching, and limited resources, satellite nodes are prone to higher bit error rates and communication delays. Additionally, the threat of Distributed Denial-of-Service (DDoS) attacks poses a serious challenge to satellite communication. These attacks are constantly evolving, hence it is crucial to develop a strategy to counter both known and unknown DDoS attacks. This article has proposed a scheme to mitigate the impact of DDoS attacks on satellite internet for reliable communication. The proposed scheme, Adaptive Link Backing Detection and Mitigation (ALBDM), leverages traffic and link features for DDoS attack detection. By implementing an adaptive model, ALBDM offers flexibility for reliable communication during DDoS assaults. A Blockchain has been integrated into the proposed scheme to mitigate DDoS attacks effectively. Simulation and performance analysis results demonstrate that the proposed ALBDM scheme significantly reduces the impact of DDoS attacks. The proposed scheme achieves a true positive rate of 99.84%, an accuracy rate of 99.3%, and a false positive rate of only 0.14%
Hydro-geomorphological modelling of leaky wooden dam efficacy from reach to catchment scale with CAESAR-Lisflood 1.9j
Get PDFLeaky wooden dams (LDs) are woody structures installed in headwater streams that aim to reduce downstream flood risk through increasing in-channel roughness and decreasing river longitudinal connectivity in order to desynchronise flood peaks within catchments. Hydrological modelling of these structures omits sediment transport processes since the impact of these processes on flow routing is considered negligible in comparison to in-stream hydraulics. Such processes are also excluded on the grounds of computational expense. Here we present a study that advances our ability to model leaky wooden dams through a roughness-based representation in the landscape evolution model CAESAR-Lisflood, introducing a flexible and representative approach to simulating the impact of LDs on reach and broader catchment-scale processes. The hydrological and geomorphological sensitivity of the model is tested against grid resolution and variability in key parameters such as leaky dam gap size and roughness. The influence of these parameters is also tested in isolation from grid resolution whilst evaluating the impact of simulating sediment transport on computational expense, model domain outputs, and internal geomorphological evolution. The findings show that simulating sediment transport increased the volume of water stored in the test reach (channel length of 160 m) by up to an order of magnitude, whilst it reduced discharge by up to 31 % during a storm event (6 h, 1-in-10-year event). We demonstrate how this is due to the leaky dam acting to induce geomorphic change and thus increasing channel roughness. When considering larger grid resolutions, however, our results show that care must be due to overestimations of localised scour and deposition in the model and that behavioural approaches should be adopted when using CAESAR-Lisflood in the absence of robust empirical validation data