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Improving access to pulmonary rehabilitation for patients with COPD treated for substance misuse in the London Borough of Islington
Get PDFChronic obstructive pulmonary disease (COPD) is a collection of conditions that cause permanent damage to the lungs. Among a range of treatment options, patients can benefit from pulmonary rehabilitation (PR) programmes involving physical exercises and education. The risk of developing COPD is higher for substance misusers than the general population. Substance misusers with COPD have more severe symptoms and poorer health outcomes than other COPD patients, and experience inequalities in accessing PR services. This project aimed to work with a local substance misuse service to increase the referrals of patients with COPD with a history of drug and/or alcohol problems to a PR programme in the London Borough of Islington. Quality improvement methods were used to explore barriers to accessing PR and to identify ways of making referral to PR easier. A series of change ideas were implemented and tested sequentially through plan–do–study–act, including updating referral systems, educating staff and improving access to diagnosis. The primary objective was to achieve 100 eligible referrals during the 14-month project period. In practice, a total of 57 patients were referred to the programme. Sustained engagement with patients was challenging, with significant attrition observed from referral to programme completion. However, there was indicative evidence of clinical improvements in dyspnoea and exercise capacity among PR completers and qualitative feedback of improved health and well-being. Although referrals numbers were less than expected, we have established an innovative respiratory care pathway for substance misusers, founded on a holistic approach to diagnosis and treatment. There are also clear pointers as to how this approach can be sustained and developed further to maximise the benefits for this cohort of patients
Paving the way to environmental sustainability: a systematic review to integrate big data analytics into high-stake decision forecasting
Get PDFBig Data Analytics (BDA) is increasingly gaining interest in supply chain management due to the incorporation of digital technology in a range of operations. It facilitates the movement of commodities and data efficiently. However, despite the numerous benefits associated with BDA, there has been limited research on the extent to which BDA can improve environmental sustainability in supply chains. In an attempt to assess the depth of our knowledge, this study undertakes a bibliometric analysis in which 155 relevant articles are retrieved. The assessment discloses the various factors driving, limiting, and stimulating the adoption of BDA in the digital supply chain through analysis and discussion. Additionally, it suggests a framework linking the factors to achieve environmental sustainability. The outcomes of the evaluation indicate that the adoption of BDA could help in realizing an eco-friendly supply chain by reducing the carbon footprint, increasing product life cycles, minimizing the cost of transportation, and reducing transport-related emissions. This research suggests that policymakers should support BDA technology adoption for the reasons identified - it assists in boosting innovation and resilience in the increasingly competitive, ever changing market and the chaotic economic conditions of some industries. Many decisions made regarding environmental sustainability call for policies that will encourage BDA use to address climate, resources, energy management and sustainability factors
Precision multi-band terahertz metamaterial biosensor with targeted spectral selectivity for early detection of MCF-7 breast cancer cells
Get PDFCancer remains one of the leading causes of mortality worldwide, with breast cancer being a particularly prevalent form. Projections estimate nearly 20 million new cases globally over the next two decades. Early detection is critical for effective treatment; however, conventional diagnostic techniques often lack the necessary sensitivity and specificity, with some methods being invasive and labor-intensive. Recent advancements in microwave imaging (MWI) have shown significant potential as efficient, non-invasive tools for monitoring various cancer types. MWI operating in the terahertz (THz) range has emerged as a promising approach for bio-sensing, offering the precision needed to differentiate between healthy and cancerous tissues by analyzing small-scale biological features. Among the methods for breast cancer detection, the identification and analysis of MCF-7 breast cancer cells are particularly significant. THz waves interact uniquely with the intrinsic properties of MCF-7 cells, making THz-based biosensors ideal candidates for diagnostic tools. However, many existing sensors are limited in key performance areas, including operating bandwidth and absorption efficiency. This study introduces a novel multi-band metamaterial (MTM)-based biosensor specifically designed for the detection of MCF-7 breast cancer cells. The sensor features a compact geometry composed of multiple resonators made from 200-nm-thick aluminium (Al) layers on a 50-μm-thick polyethylene terephthalate (PET) substrate. With dimensions of only 198 × 198 μm², the proposed device is exceptionally compact. It operates in the 0.5 THz to 1.6 THz frequency range and achieves near-perfect absorption rates (>99%) across multiple bandwidths. These results are achieved through precise tuning of the sensor's geometry and architectural optimization, significantly enhancing its sensitivity for cancer detection. Comprehensive validation of the sensor is performed using full-wave electromagnetic analysis, which includes evaluating electric and magnetic field distributions, surface currents, and scattering parameters. Extensive benchmarking demonstrates the device’s superior performance compared to state-of-the-art biosensors, excelling in metrics such as quality-factor, figure of merit (FOM), and absorption efficiency. Additionally, the proposed sensor has been integrated into an MWI system to evaluate its practical application. The device successfully discriminated against subtle changes in the refractive index of biological tissues, confirming its ability to detect MCF-7 cells effectively. These findings highlight the sensor's suitability as a non-invasive, early-stage diagnostic tool for breast cancer
An Interpretative Phenomenological Analysis of the participants’ experience of Anorexia Nervosa and what they feel aided recovery
Get PDFLiterature Review: The reviewed literature brings together, summarises and critiques the research that is available on Anorexia Nervosa, and provides a review of the nature, quality and extent of research in relation to the topic and gaps in the current research are then highlighted.
Rationale: the aim of the research was to understand the lived experience of those who had recovered from Anorexia Nervosa and to understand what they felt had aided their recovery with the hope that this could provide valuable information to those working with individuals who are experiencing the disorder. Recovery is defined as no longer meeting the diagnostic criteria outlined in the DSM-5-TR (2022). The participants in this study had been in recovery for over three years; this duration was selected because most research focuses on recovery within the first twelve months post-treatment, during which relapse is most common. Consequently, insights were gathered from individuals experiencing long-term recovery.
Method: The data obtained from the interviews was analysed using Interpretative Phenomenological Analysis (Smith, Larkin and Flowers, 2015; Smith and Nizza, 2022), to look at the lived experience of the participants.
Findings: The main findings of the study are that the experience of Anorexia Nervosa had taken away the participants’ hope of a life without the disorder and the eventual move to recovery became possible once they were motivated to make the necessary changes. The move to recovery was subjective to the individual but was found to be possible via early diagnosis before the disorder becomes entrenched; access to psychological treatment; the establishment of a good therapeutic alliance based on mutual trust and understanding; to instill hope, that they can overcome the disorder and to be helped by the professionals whose care they were in, to envisage a life outside of the Anorexia Nervosa, where friendships are re-established, identity is recovered and a new life can begi
Vitamin D analysis for sustainable healthcare in Inner London Borough
Get PDFVitamin D is vital for bone health, immune system support, and muscle function. Deficiency in Vitamin D is widespread, with up to 65% of individuals in certain populations, including Black students at London Metropolitan University, UK, being affected. This study focuses on the need for a deeper understanding of Vitamin D prescription patterns, specifically within an inner London borough, using advanced data analytics. Previous analysis, such as ones conducted by OpenPrescribing.net, has investigated NHS prescription data but lacked a focused examination on Vitamin D. Our study introduces a novel computational approach, integrating NHS datasets from 2013 to 2023. We developed a web‐hosted dashboard using Python, Flask, Cesium, PowerBI, and libraries such as Pandas, Scikit‐learn to provide real‐time data visualization and predictive analytics. Our methodology involved API‐driven ingestion of large‐scale data, focusing on Vitamin D prescriptions in a borough, and mapping this against patient numbers. We used feature manipulation and model training to gain insights into prescription counts, dosages, medication types, and formulations. This interactive platform supports dynamic reporting through PowerBI and Cesium. Our findings reveal significant variations in prescription patterns among GP surgeries influenced by socioeconomic factors. This interdisciplinary project, in future collaboration with local GP federations, United Kingdom, enhances computational health data analysis and aims to inform better prescription practices and healthcare policies, ultimately improving policy practice and public health outcomes
High gain narrow beam MIMO array antenna operating at n260 band for millimeter wave applications
Get PDFThis paper introduces a novel four-port antenna array designed for n260 band operation, specifically addressing challenges like path loss fading and multipath effects commonly encountered in millimeter-wave frequencies in urban settings. The proposed 4 × 4 MIMO antenna array operates in the n260 band, offering high gain and a narrow beam, with an extended design enabling spatial and pattern diversity to mitigate multipath effects effectively. The single-element antenna combines elliptical and circular rings fed by a quarter-wave transformer. Its fundamental resonance frequency of 30 GHz is suppressed by integrating circular rings in the radiator and a slot in the ground plane, which enhances its first harmonic at 38.5 GHz while generating vertical polarization. The array antenna improves the fractional bandwidth (FB) to 8.4%, with a frequency range of 36.76-39.92 GHz, and achieves dual broadside beams at ±37◦ angles with a gain of 16.7 dBi. Additionally, it exhibits exceptionally low cross-polarization (−80 dB), minimizing cross-talk effects. The MIMO configuration demonstrates excellent isolation (|S21| > 26 dB and 31.2 dB) while maintaining similar FB and radiation pattern characteristics as that of the array antenna. This robust design, incorporating both spatial and pattern diversity, makes it highly suitable for 5G wireless applications
AI-powered system for an efficient and effective cyber incidents detection and response in cloud environments
Get PDFThe growing complexity and frequency of cyber threats in cloud environments call for innovative and automated solutions to maintain effective and efficient incident response. This study tackles this urgent issue by introducing a cutting-edge AI-driven cyber incident response system specifically designed for cloud platforms. Unlike conventional methods, our system employs advanced Artificial Intelligence (AI) and Machine Learning (ML) techniques to provide accurate, scalable, and seamless integration with platforms like Google Cloud and Microsoft Azure. Key features include an automated pipeline that integrates Network Traffic Classification, Web Intrusion Detection, and Post-Incident Malware Analysis into a cohesive framework implemented via a Flask application. To validate the effectiveness of the system, we tested it using three prominent datasets: NSL-KDD, UNSW-NB15, and CIC-IDS-2017. The Random Forest model achieved accuracies of 90%, 75%, and 99%, respectively, for the classification of network traffic, while it attained 96% precision for malware analysis. Furthermore, a neural network-based malware analysis model set a new benchmark with an impressive accuracy rate of 99%. By incorporating deep learning models with cloud-based GPUs and TPUs, we demonstrate how to meet high computational demands without compromising efficiency. Furthermore, containerisation ensures that the system is both scalable and portable across a wide range of cloud environments. By reducing incident response times, lowering operational risks, and offering cost-effective deployment, our system equips organizations with a robust tool to proactively safeguard their cloud infrastructure. This innovative integration of AI and containerised architecture not only sets a new benchmark in threat detection but also significantly advances the state-of-the-art in cybersecurity, promising transformative benefits for critical industries. This research makes a significant contribution to the field of AI-powered cybersecurity by showcasing the powerful combination of AI models and cloud infrastructure to fill critical gaps in cyber incident response. Our findings emphasise the superior performance of Random Forest and deep learning models in accurately identifying and classifying cyber threats, setting a new standard for real-world deployment in cloud environments
Microwave-based breast cancer detection using a high-gain Vivaldi antenna and metasurface neural network approach for medical diagnostics
No full textThis paper presents a novel technique for detecting tumors in human breasts using a single high-gain antenna and a metasurface (MTS) layer. An artificial neural network (ANN) is employed to classify detected tumors as benign or malignant based on the permittivity of the tissue. The detection and classification process leverages the contrast in dielectric properties between normal and abnormal biological tissue, utilizing the actual permittivity as a distinguishing factor. This study highlights the effectiveness of the proposed technique in accurately detecting and localizing malignant tumors within human breasts. Electromagnetic analysis is conducted using voxel datasets derived from human models to validate the approach. Tumor localization is achieved with high precision based on the Specific Absorption Rate (SAR) magnitude. The study considers various fat layer thicknesses (10–100 mm) and tumor radii (2.5–10 mm), addressing scattering effects comparable to the wavelength of the applied microwave radiation. The proposed Vivaldi antenna operates at 3.5 GHz, achieving a gain of 15.5 dBi with a half-power beamwidth in the E-plane of ±12°. Results demonstrate minimal average errors and high-performance indices (PI) for fat thickness (0.1%, 90%), tumor size (0.06%, 94%), and tumor classification (0.11%, 89%). The experimental and simulation results exhibit strong agreement, confirming the feasibility and potential of the proposed antenna system for medical diagnostics
Dietitians' adherence and perspectives on the European Association for the Study of Obesity (EASO) and the European Federation of the Associations of Dietitians (EFAD) recommendations for overweight and obesity management: a mixed-methods study
Get PDFIntroduction:
Recent guidelines developed by the European Association for the Study of Obesity (EASO) and the European Federation of the Associations of Dietitians (EFAD) focused on the dietetic management of obesity in adults. The present study aimed to explore the perspectives of healthcare professionals regarding these guidelines.
Methods:
In total, 85 registered dietitians/nutritionists from Greece, the Netherlands, the Republic of Ireland, and the United Kingdom completed an online survey, and 10 were interviewed, in February–March 2023. Demographic data were also collected.
Results:
Awareness of the EASO-EFAD guidelines among registered dietitians/nutritionists was moderate (57.6%), but only 20% had read them in full. Dietitians with higher education and relevant experience were more likely to have read the guidelines. Less than half reported that key evidence-based recommendations, such as individualized medical nutrition therapy and intensive behavioral interventions, are already included in national guidance. Recommendations like portfolio or DASH diets, partial meal replacements, and calorie restriction were less commonly part of national guidance/usual practice. A small percentage of participants described their adoption of several nutritional approaches novel to them. These included the portfolio dietary pattern, partial meal replacements, and intermittent fasting or continuous calorie restriction. For some Irish dietitians, prioritizing weight as the main outcome conflicted with their emphasis on overall health and individualized nutrition therapy. Other barriers of recommendation implementation included exclusive availability in English, rapid changes in obesity management, staffing shortages, limited multidisciplinary collaboration, and inconsistent knowledge among healthcare providers.
Conclusions:
The present study identified gaps in the adoption of the EASO-EFAD guidelines into dietetic/clinical practice. EFAD will develop strategies to disseminate these guidelines at different levels of stakeholders (national/local authorities, dietitians/nutritionists, and patients)
Enabling collaborative forensic by design for the internet of vehicles
Get PDFThe progress in automotive technology, communication protocols, and embedded systems has propelled the development of the Internet of Vehicles (IoV). In this system, each vehicle acts as a sophisticated sensing platform that collects environmental and vehicular data. These data assist drivers and infrastructure engineers in improving navigation safety, pollution control, and traffic management. Digital artefacts stored within vehicles can serve as critical evidence in road crime investigations. Given the interconnected and autonomous nature of intelligent vehicles, the effective identification of road crimes and the secure collection and preservation of evidence from these vehicles are essential for the successful implementation of the IoV ecosystem. Traditional digital forensics has primarily focused on in-vehicle investigations. This paper addresses the challenges of extending artefact identification to an IoV framework and introduces the Collaborative Forensic Platform for Electronic Artefacts (CFPEA). The CFPEA framework implements a collaborative forensic-by-design mechanism that is designed to securely collect, store, and share artefacts from the IoV environment. It enables individuals and groups to manage artefacts collected by their intelligent vehicles and store them in a non-proprietary format. This approach allows crime investigators and law enforcement agencies to gain access to real-time and highly relevant road crime artefacts that have been previously unknown to them or out of their reach, while enabling vehicle owners to monetise the use of their sensed artefacts. The CFPEA framework assists in identifying pertinent roadside units and evaluating their datasets, enabling the autonomous extraction of evidence for ongoing investigations. Leveraging CFPEA for artefact collection in road crime cases offers significant benefits for solving crimes and conducting thorough investigations