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A two‐phase approach to identifying HFpEF in heart failure patients: Risk score evaluation and decision tree development
Get PDFAims: Heart failure (HF) with preserved ejection fraction (HFpEF) poses significant diagnostic challenges due to its complex aetiology and overlapping symptoms with other HF types. The heterogeneity of HFpEF, compounded by frequent comorbidities, complicates diagnosis. This study aimed to enhance HFpEF prediction through a two‐phase approach: a simplified risk score and a decision tree model. Methods and results: In Phase 1, an 8‐point risk score based on accessible clinical parameters was developed. In Phase 2, we conducted comprehensive predictive modelling using decision tree analysis. Data from 560 HF patients were analysed. It achieved an accuracy of 63.13% (sensitivity: 62.87%, specificity: 54.24%). In Phase 2, a decision tree model using broader clinical variables improved accuracy to 73.04% (sensitivity: 53.89%, specificity: 81.17%). Conclusions: This dual framework provides tools for both quick screening and detailed risk stratification in various clinical settings
Understanding work and study demands of degree apprentices using Conservation of Resources Theory
Get PDFDegree apprenticeships are a recent innovation in the UK. The overarching goal of the apprenticeship is to achieve a degree, while working and advancing careers. This involves apprentices and employing organisations investing in resources including workplace support for education and new skills. This study interviewed apprentices (n=28) approaching the completion of their apprenticeship (and degree) to explore this investment through the lens of Conservation of Resources Theory; specifically, whether apprentices had achieved their goals and how they had managed the challenges of balancing work, life, and study. This unique approach considered how apprentices gained and conserved resources conducive to reducing workplace stress, to gain insights into apprentices’ experiences, resulting in a new resource model for apprenticeships. Goals achieved included the degree award, promotions, pay rises and increased status. We found that skills acquisition, alignment of work/ study, and high levels of support increased the apprentices’ resources. Workplace mentors had a crucial role to play, but their approach varied widely, indicating that improving key features of apprenticeship implementation could conserve apprentices’ resources, enhancing apprentice wellbeing. The significance of the findings is a new resource model for understanding the intersection of work and study
A Stochastic Prototypical Network for Few-Shot Intrusion Detection in CAN-Based IoV Network
Get PDFThe Controller Area Network (CAN) acts as the backbone of intra-vehicle communication in modern Internet of Vehicles (IoV) systems, enabling real-time coordination among critical automotive subsystems. Despite its widespread adoption, CAN lacks essential security mechanisms such as encryption and message authentication, rendering it highly vulnerable to cyberattacks that can jeopardize vehicle safety and operational integrity. Developing an effective Few-Shot Learning (FSL)-based Intrusion Detection System (IDS) for CAN networks presents challenges due to data scarcity, noisy traffic, dynamic attack patterns, and the need for real-time efficiency. Existing FSL approaches often rely on deterministic models that struggle to capture the uncertainty and variability inherent in CAN network traffic. To address these challenges, we propose a Stochastic Prototypical Network based on a Random Neural Network (RaNN) for few-shot intrusion detection in CAN-based networks. RaNNs are inherently stochastic, enabling them to model uncertainty and variability in network traffic. By integrating RaNN with the prototypical network, the proposed framework computes stochastic prototypes that represent the distribution of normal and attack behaviors, improving robustness in noisy and dynamic environments. Additionally, the framework quantifies uncertainty in its predictions, enabling the system to flag ambiguous cases for further analysis, thereby reducing the risk of both false positives and negatives. The proposed approach demonstrates high classification performance across all FSL scenarios, achieving a maximum accuracy of 99.17% in a 15-shot configuration. The framework shows impressive computational efficiency with millisecond inference times and minimal training overhead, making it suitable for real-time deployment
Carbon-Aware Edge Computing for Internet of Everything Networks: A Digital Twin Approach
No full textThe rapid growth of edge computing has enabled low-latency and high-efficiency processing for a wide range of applications; however, it also leads to significant energy consumption and carbon emissions. In this context, this study investigates a CO2 emission minimization problem in a digital twin-aided edge computing system, aiming to optimize task offloading decisions, transmit power, and processing rates of Internet of Things (IoT) devices. To address the formulated mixed-integer nonlinear programming problem, we propose two solutions: 1) an alternating optimization method based on the successive convex approximation framework and 2) a deep reinforcement learning (DRL) approach. Extensive simulations validate the effectiveness of the proposed solutions, demonstrating significant reductions in CO2 emissions, robust optimization performance, and superior results compared to benchmark schemes. The findings highlight the feasibility of integrating advanced optimization and artificial intelligence-driven techniques to achieve environmentally sustainable and high-performance edge computing systems, paving the way for greener technological innovation
Shoreline change assessment in rapidly urbanizing coastal megacities using geospatial techniques
Get PDFCoastal megacities face significant threats from various climate-induced hazards, including cyclones, storm surges, rising sea levels, coastal landslides, and floods. In recent decades, rapid urbanization and a range of anthropogenic pressures have exacerbated the vulnerability of these coastal regions. To assess the current level of coastal vulnerability, we focused on two Asian coastal megacities: Shenzhen and Shanghai. Using the Digital Shoreline Analysis System (DSAS) to evaluate coastal erosion vulnerability and shoreline changes from 1990 to 2022, we employed Shoreline Change Envelope (SCE) and Net Shoreline Movement (NSM) to measure the distance of shoreline change, while the rate of shoreline change was calculated with the End Point Rate (EPR). To simplify the DSAS analysis and highlight significant findings, Shenzhen's coastline was divided into two sections: The western coast (C1) and the eastern coastline (C2). The SCE results indicated a noteworthy seaward shoreline shift of 4.5 km in section C1 from 1990 to 2010, while section C2 experienced a significant alteration, extending 2.5 km seaward during the same period. In contrast, Shanghai exhibited a remarkable shoreline change, extending 6.5 km seaward between 1990 and 2010. NSM analysis revealed that within region C2, the maximum recorded erosion from 1990 to 2022 was 920 m, with the furthest observed accretion reaching 2,483 m. Additionally, results indicated that in section C1, the lowest shoreline change rate was −11.9 m/year, illustrating erosion, while Shanghai's minimum shoreline change rate was −3.04 m/year, also indicating a trend of erosion. The findings from this study, combined with the vulnerability maps, will aid policymakers and decision-makers in developing strategies to enhance the quality of life for coastal communities
Multifaceted enhancement of piezoelectricity and optical fluorescence in electrospun PVDF-ceria nanocomposite
Get PDFThis study investigates the enhancement of piezoelectric and optical fluorescence properties in electrospun polyvinylidene fluoride (PVDF) nanocomposite membranes doped with cerium oxide (Ce3+) at varying weight percentages. An optical characterisation using absorbance analysis found a blue shift in the bandgap of the ceria NPs, which also enhanced UV absorption in the PVDF polymer. At some additive doses, luminosity analysis demonstrated an incremental fluorescence impact. However, above a certain point, additional increases seemed to have a quenching effect, which decreased fluorescence. FTIR based analysis revealed the enhanced β sheets content to 61.75% in the sample of PVDF with a ceria 5 wt%. The fabricated nanofiber membrane displayed an average fiber diameter of around 108 nm. XRD analysis confirms that the incorporation of Ce3+ significantly promotes the formation of the β-phase in PVDF, thereby improving its piezoelectric response. Additionally, water contact angle measurements indicate increased hydrophobicity in the nanocomposite membranes, expanding their applicability in sensing and energy harvesting applications. ICP-OES and XRF analysis confirm that Ce was successfully incorporated with the PVDF chain. The dual role of ceria as both a nucleating agent for β-phase formation and an optical fluorescence enhancer highlights its potential for the development of multifunctional nanocomposites. This work presents a novel approach to engineering PVDF-based materials with enhanced piezoelectricity and optical fluorescence for advanced technological applications. This ultrasensitive PVDF with a ceria 5 wt% nanogenerator demonstrated pronounced piezoactivity, generating a maximum of 9 V with 3 N load at 1.5 Hz frequency which is almost three times of the output generated by pure PVDF. The formed oxygen vacancies according to tri-valent cerium ions, which have been showed through optical characteristics, supports the nucleation of PVDF chains around ceria NPs. The resultant PVDF/ceria nanomembrane demonstrated a remarkable maximum power density of 89 mW/m2, demonstrating its load-bearing capability. With its dual functionality as an optical sensor and an energy harvesting unit, this adaptable nanocomposite shows potential for use in multifunctional devices
The social conception of space of birth according to women with positive birth experiences: A trans-European study
Get PDFBackground The social space of birth—the birth environment, its occupants, and the human activities taking place—is interconnected with birth experiences. Aim To investigate how the reality of the social space of birth affects women’s positive birth experiences. Methods We combined open-text responses to the Babies Born Better survey from 3633 postpartum women in Austria, Belgium, the Czech Republic, Germany, Spain, the Netherlands, and the United Kingdom and 39 interview transcripts from Czech and Dutch postpartum women. We conducted a textual and thematic analysis. Findings Three themes and 11 categories were generated: (1) Exercising fundamental human agency in the birth space consists of the categories: ‘exercising rights’, ‘the protection of human vulnerability’, and ‘the freedom to be authentic’, which women regard as prerequisite components of the birth space. (2) Regulatory frameworks & care philosophies in maternity services, including the categories ‘(financial) regulations’, ‘values of the care provider and the institution’, and ‘model of care’, are regarded as attributes of the birth space. Theme (3) Building a nest for comfort and connection comprises the categories ‘relational and affective atmosphere during labour & birth’, ‘performative atmosphere during labour & birth’, ‘shelter’, ‘implicit and explicit tacit doing & being’ and ‘symbol of deeper meaning’. Discussion/Conclusion The reality of the birth space of women with positive birth experiences consists of human rights and birth rights, the quality of interactions with care providers during labour and birth in a relationship-centred and relation-continuity model of care, and a place to retreat from the world
A research toolbox for regional data collection to support the conservation of large batoids: A case study on the critically endangered flapper skate (Dipturus intermedius)
Get PDFElasmobranchs, specifically skate species (superorder Batoidea), are at risk of extinction, with over one‐third currently listed as Endangered, exacerbated due to their k‐selected life strategy. A regional conservation approach is required to support the collection of rigorous, species‐specific data alongside collaborative efforts across sectors and jurisdictions. Skate species that extend beyond jurisdictional boundaries encounter additional complexities from divergent national legal frameworks, monitoring requirements, and conservation priorities, resulting in inconsistent data collection. Here we present an innovative research “toolbox,” initially devised for the Critically Endangered flapper skate (Dipturus intermedius) in the North‐East Atlantic, but applicable to most demersal elasmobranchs. This toolbox offers a systematic approach (Why, What, Who, Where, and When?) to obtain critical information for the conservation of elasmobranchs, with a focus on standardization and cross‐border collaboration. Recent advancements in understanding flapper skate ecology highlight the potential for regional conservation initiatives, emphasizing the importance of coordinated actions, and serve as an illustrative example within the context of the “toolbox.
The SAbyNA platform: a guidance tool to support industry in the implementation of safe- and sustainable-by-design concepts for nanomaterials, processes and nano-enabled products
Get PDFSimple, cost-effective and reliable methods are needed for pragmatic, flexible, safe and sustainable evaluations at early stages of product (chemical/material) development. This is especially true for nanoforms and nano-enabled products, for which guidance on the application of validated methods and tools for the assessment and management of safety and sustainability is still lacking. The SAbyNA guidance platform fills these gaps in the following ways: by integrating i) informative modules covering the needs of all stakeholder profiles (i.e., industry, consultants, RTOs, and regulatory bodies) by guiding them in the choice of methods, models and tools for exposure and hazard assessment, as well as in the selection of specific safe-by-design interventions; and ii) assessment modules for a screening-level evaluation of environmental sustainability and costs and for a screening and detailed safety assessment of nanoforms and nano-enabled products along their life cycle. The potential of this digital tool to support different stakeholders towards safer and more sustainable developments is demonstrated in a real case study: a nano-enabled 3D-printed vacuum cleaner plastic component composed of single-walled carbon nanotube–polycarbonate composites with antistatic properties. This study shows how a user inputs data to perform a screening assessment on an additive manufacturing case study, and the digital platform provides the user with some safe-by-design recommendations, such as reducing the fiber length or rigidity or changing process parameters to reduce emissions. Hazard, exposure, costs, sustainability and functionality case study data were added in the detailed assessment module of the platform to check whether the implemented safe-by-design intervention was able to improve the safety profile of this nano-enabled product without affecting sustainability and functionality performances. This study also demonstrated the added value of using the SAbyNA guidance platform at the early stage of the nano-enabled product development for the quantification and visualization of safety, sustainability, cost and functionality aspects of nano-enabled products and processes
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