University of the West of Scotland
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Processing–property relationships in 3D printed PLA/graphene composites for synergistic enhancement of mechanical strength and electrical conductivity
Full text linkThe integration of nanofillers such as graphene into polylactic acid (PLA) has attracted significant attention for enhancing the performance of fused filament fabricated (FFF) components. Despite these advances, the electromechanical properties of PLA–graphene composites remain insufficient for many functional applications, particularly in electronics and other industrial sectors. To address this limitation, the present study investigates how a broad range of fused filament fabrication (FFF) process parameters, including print orientation, raster angle, infill pattern, layer height, infill density, and printing speed, collectively influence the electromechanical performance of PLA-graphene composites. Using a Taguchi L18 orthogonal array, the study systematically evaluates and optimizes tensile, flexural, impact, and electrical conductance responses. The results demonstrate that a cubic infill pattern yields superior mechanical and conductive properties. At the same time, flat printing orientation enhances tensile and impact strength, and on-edge orientation improves impact strength and conductance. Optimal infill densities of 70 %, 80 %, and 90 % were identified for maximizing tensile, flexural, and impact strengths, respectively. The optimized specimen achieved a tensile strength of 51.91 MPa, flexural strength of 62.6 MPa, impact strength of 59.38 J/m, and conductance of 19.12 μS. Grey relational analysis further identified the most effective parameter combination for simultaneous multi-response optimization. Overall, this research provides a comprehensive understanding of process–property relationships in PLA–graphene composites and establishes a pathway for fabricating multifunctional FFF parts with improved electromechanical performance
Smart tourism in the age of IoT:a futures study of Fars Province, Iran
Full text linkPurpose This study aims to examine the key factors influencing the successful implementation of Internet of Things (IoT) technologies in transforming tourism destinations into smart ecosystems. Through an in-depth case study of Fars Province, a prominent tourist hub in Iran, this research provides an empirical framework for understanding the drivers and barriers of IoT adoption in smart tourism.Design/methodology/approach A multi-method approach is employed in this study. Data are collected through a systematic literature review, interviews, and questionnaires. Scenario Wizard and MICMAC software are used for data analysis, with MICMAC identifying critical variables that impact IoT implementation. The study further develops two future scenarios based on favorable and critical conditions surrounding IoT adoption in tourism.Findings The MICMAC analysis reveals 14 key variables that significantly influence the implementation of IoT technologies for smart tourism in Fars Province. These variables serve as the foundation for constructing two potential future scenarios, offering insights into both favorable and challenging pathways for IoT adoption.Originality/value This research provides tourism stakeholders, particularly decision-makers in the field, with valuable tools for long-term strategic planning. By identifying the key factors driving IoT adoption and exploring potential future outcomes, the study empowers stakeholders to make informed decisions and actively contribute to the development of smart tourism ecosystems, promoting sustainable and prosperous tourism destinations
AI-powered precise diagnosis and automated nail disease detection using a fused CNN-CapsNet model
Full text linkNail disease classification is a crucial task in dermatology, aiding in the early diagnosis and treatment of various conditions. In this study, we leverage an open-access dataset from Kaggle containing 3,835 images and apply data augmentation techniques, expanding the dataset to 11,505 images to improve model generalization. We propose a CNN-based deep learning model and evaluate its performance on the augmented dataset. To further enhance classification accuracy, we fuse the proposed CNN model with a Capsule Network (CapsNet), leveraging its ability to capture spatial hierarchies and complex relationships between image features. Both models are trained and evaluated, followed by a visualization of classification results. The fused CNN-CapsNet model outperforms the standalone CNN model, achieving an overall accuracy of 98.5%, demonstrating precise and secure AI-powered nail disease diagnosis, ensuring model robustness. This research highlights the advantages of combining CNNs with Capsule Networks for improved medical image analysis and classification
Breathlessness services and specialist palliative care for advanced chronic obstructive pulmonary disease:a narrative review
Full text linkChronic obstructive pulmonary disease (COPD) is a progressive condition associated with high distress, functional decline, and reduced quality of life. As the disease advances, palliative care becomes increasingly pertinent to address not only physical symptoms, such as breathlessness, but also the emotional and social challenges experienced by individuals with COPD and their caregivers. Emerging evidence supports the benefits of earlier palliative care integration across the disease trajectory, and there are various models of palliative care for people with COPD. This narrative review synthesizes current evidence on supportive breathlessness clinics and specialist palliative care services for individuals living with COPD. The findings highlight that while supportive services significantly enhance patients’ coping, confidence, and psychological well-being, their availability and integration into routine care remain limited. Interventions that include home visits, personalized approaches, and peer interaction are particularly valued by people with COPD and are associated with reduced distress and improved self-management. Moreover, interpersonal connection and regular empathetic contact with healthcare professionals emerged as central therapeutic mechanisms. Despite these promising outcomes, many studies face methodological limitations, including small sample sizes, a lack of condition-specific focus, and challenges in evaluating psychosocial impacts. Specialist palliative care remains underutilized in COPD populations, and access is often delayed or absent compared to people with malignant diseases. Integrating respiratory and palliative expertise in flexible, community-based services offers a promising direction to improve quality of life and end-of-life care for this underserved population. To ensure equitable care, future efforts should prioritize timely access to supportive care, greater integration with respiratory services, and the inclusion of people with COPD in palliative care planning and research. Future research should focus on inclusive and patient-centered approaches which explore how to sustainably deliver patient-centered and multidisciplinary support across care settings
<i>Candidozyma auris</i> reported in Scotland:a call for vigilance amid global rise
Full text linkCandidozyma auris (formerly Candida auris) is an emerging pathogenic yeast associated with healthcare outbreaks worldwide. Despite increasing reports across Europe, no published data have previously described cases in Scotland. Here, we report the first detections of C. auris in Scotland, as submitted to ARHAI Scotland. Eight cases (seven colonisations, one infection) were identified to date across four NHS Scotland boards, all linked to repatriation or recent hospitalisation abroad. To contextualise these findings, we reviewed publicly available literature and surveillance data for Western and Northern Europe, identifying considerable variation in case numbers and highlighting Scotland’s position among countries with the lowest reported cases. All Scottish cases were imported, underscoring the importance of targeted screening of patients with international healthcare exposure. These findings inform preparedness planning and support recommendations for strengthened surveillance to prevent onward transmission
‘Out here, you live and die by your reputation’:organised crime and dark capital in <i>Star Wars: Outlaws</i>
Full text linkThis paper examines the video game Star Wars: Outlaws (2024), exploring its portrayal of organised crime and associated forms of capital. The game follows Kay Vess, a young street thief seeking a fresh start, whose journey is shaped by an in-game reputation system that enhances gameplay immersion and deepens narrative complexity. Analysing data from 53 dedicated gameplay sessions, this paper explores Kay’s transition from emergence and survival to ascent, agency, and autonomy in the underworld. Critically assessing the extent to which the game encourages the accumulation and deployment of street capital and criminal capital, the paper introduces the concept of dark capital as a lens for understanding organised crime. It concludes by exploring the potential contribution of dark capital to the policing of organised crime
<i>Botryococcus braunii</i> lipid production pathways and biorefinery potential
Full text linkBotryococcus braunii is a colonial microalga recognized for its ability to produce and secrete long-chain hydrocarbons, positioning it as a promising feedstock for biofuel and bioproduct applications. This review synthesizes current knowledge on race-specific hydrocarbon profiles, genetic markers for classification, and the biochemical pathways underlying lipid biosynthesis. It evaluates cultivation strategies alongside stress-based approaches to enhance productivity. Lipid recovery technologies are discussed with emphasis on sustainable, non-destructive methods such as milking and switchable solvents, which reduce energy demands and preserve cell viability. The integration of these processes within biorefinery frameworks highlights opportunities for the co-production of fuels and high-value compounds. By linking molecular insights with process engineering, this work underscores the potential of B. braunii to contribute to sustainable energy systems
A robust E learning recommendation system based on novel interval valued bipolar fuzzy hypersoft set theory
Full text linkUnderstanding bipolar information is crucial as it enables individuals to make informed decisions that consider both extremes of a spectrum, leading to more balanced and effective outcomes. Interval-valued bipolar fuzzy set (IVBFS) has already been introduced in the literature as a great decision-making tool that can capture interval-valued bipolar information to properly address uncertainty. In this article, we introduce a hybrid of Interval-valued bipolar fuzzy set (IVBFS) and bipolar hypersoft sets (BHSS) called interval-valued bipolar fuzzy hypersoft set (UVBFHSS), which merges the capabilities of IVBFS and BHSS. The rationale behind the design of the presented data structure is to manipulate and process information in decision-making scenarios when the data is bipolar, has multiple attributes that need to be addressed up to a sub-attributive level to get a proper representation of the data provided, and needs to be presented in the form of intervals. In (IVBFHSS), two hyper soft sets (HSSs) are used, one providing positive interval-valued membership information and the other providing negative interval-valued membership information. We outline the essential features and basic operations of (IVBFHSS) in this paper, examining its commutative, associative, distributive, and De Morgan laws to ensure a comprehensive analysis. To demonstrate the significance of (IVBFHSS), we develop a preferential decision support algorithm for selecting the best alternative in e-learning, such as identifying the most suitable instructional method, which can effectively be formulated as a Multi-Attribute Decision-Making (MADM) problem. This approach allows for the systematic evaluation of various alternatives based on multiple parameters and sub-parameters, enabling a rational and well-informed decision. This algorithm helps select the best alternative from a given set of options, leveraging the versatile nature of (IVBFHSS). The presented study conducts both computation-based and structural comparisons to evaluate the adaptability and reliability of the proposed framework
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/
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