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The resonance of Mike Jackson's work with the use of systems ideas in community operational research
Get PDFThe body of work of Mike Jackson covers several major themes in OR/Systems Thinking and articulates key aspects of Critical Systems Thinking; with an interest throughout in applications to complex social challenges. In this paper, as a direct response to this Festschrift, and acknowledging his contribution to Community OR, five active UK-based researchers have engaged in their own process of community-based learning in order to articulate the ways Jackson's work resonates with their contemporary research and practice. The researchers used a variation of the Delphi method to reflect first on the ways that the body of work of Jackson resonated with their practice and research agendas. This produced a framework of ideas. Examples from the UK and overseas are then provided to illustrate these points. Ultimately, the researchers used these experiences and reflections to produce a series of statements for developing Community OR practice (and theory)—reflecting and extending Jackson's work
Agricultural transformation: Exploring the impact of digitalization, technological innovation and climate change on food production
Get PDFAgricultural transformation is crucial for achieving the United Nations Sustainable Development Goals. Sustainable food production has become a key driver of this transformation, with China playing a vital role as a major global food producer. We investigate the long- and short-term impacts of technological innovation (R&D investment), digitalization (Internet and mobile phone use), and temperature changes on food production in China. We confirm long-run equilibrium relationships between these variables using autoregressive distributed lag (ARDL) bounds testing. Findings indicate that the long-term usage of mobile phones and the Internet significantly increases food production. Similarly, long-term R&D expenditures significantly enhance food production, as long-term temperature increases reduce food production. Governments should facilitate information and communications technologies, research and development, and affordable financing to achieve food sustainability and adapt to climate change
A Multi-Tier Offloading Optimization Strategy for Consumer Electronics in Vehicular Edge Computing
Get PDFIn the domain of consumer electronics, vehicular edge computing (VEC) technology is emerging as a novel data processing paradigm within vehicular networks. By sending tasks related to vehicular applications to the edge, this model makes it easier for computing power to be spread out. This lets interactive services respond quickly. Nevertheless, the computational resources at edge servers are inherently limited and often tasked with handling multiple concurrent operations. The inefficacious allocation of these resources significantly impairs the efficiency of task offloading. Additionally, indiscriminate offloading could overwhelm the servers, detrimentally impacting the performance of subsequent tasks. To circumvent these challenges, this study introduces a multi-tier offloading model predicated on game theory principles. This framework aims to optimize resource utilization at the edge while accounting for server load to ensure the timely execution of latency-sensitive tasks. To evaluate this model, this paper created a simulation environment specifically for video game tasks in consumer electronics. The experimental results show that the multi-tier offloading model can effectively relieve the load pressure on the edge server. The task failure rate of the multi-tier offloading model remains at the lowest level compared with several state-of-the-art algorithms, significantly reducing the execution delay of tasks and being able to meet the requirements of consumer electronics applications
Engaging Eastern and Central European migrants with newly and/or unsettled immigration status in palliative care services and research
Get PDFInterim report January 2022 on online workshop 29th April 2021
Assessing the Confidence of Graduating Pre-Registration Nurses to Use and Embed Respiratory Clinical Skills in Practice: A Mixed Methods Analysis
Get PDFBackground: The incidence of respiratory disease is increasing; all nurses will care for respiratory patients during their career.There is disparity in the teaching of pre-registration respiratory nursing education. In the UK, revised Nursing and Midwifery Councileducational standards provided the opportunity to review provision of UK respiratory nursing education.This study was conducted to assess and make educational recommendations on the teaching, learning and student nurses’ confidenceto apply respiratory nursing skills in clinical practice at the point of qualifying (registration).Method: A cross-sectional survey design. The survey was distributed to final year pre-registration nursing students in the UK viasocial media over a 10-week period (summer 2021), with 152 student responses across 29 Universities. Where available on socialmedia, nursing departments in UK universities were contacted by one of the authors [NJR] to aid dissemination.Results: Less than half of the students felt completely/fairly confident about their knowledge and understanding of respiratoryanatomy and physiology (46.1%), respiratory pathophysiology (32.2%). Line of argument synthesis constructed four themes, aroundstudent confidence, aligning the quantitative and qualitative data:• Disparity in teaching methods, application, and position in the programme,• Positive respiratory learning experiences in clinical practice,• Insufficient time and narrow disease scope of respiratory education,• Application of personal learning experiences of respiratory illness.Conclusion: We report lower levels of student confidence in key respiratory knowledge and skills, with disparity in UK HEI teachin
Industry guidance document: Working Safely with Nanomaterials in Research & Development
No full textA comprehensive guidance document on factors relating to establishing a safe workplace and good safety practices when working with nanomaterials - written in collaboration between UK Government and UK research institutes
iSIMPATHY: a multinational pre–post non-randomised intervention study transforming medication review
Get PDFBackground: Taking multiple medicines can be appropriate but has the potential to be problematic. The Implementing Stimulating Innovation in the Management of Polypharmacy and Adherence THrough the Years (iSIMPATHY) project used the 7-Steps person-centred approach for medication reviews, supporting patients and clinicians to define and achieve realistic goals for drug treatment, and helping enable patients to lead healthy and active lives. Objective: To assess the impact of pharmacist-led comprehensive person-centred medication reviews using the 7-Steps methodology. Method: iSIMPATHY sought to transform the approach to optimisation of medicinces through the delivery of person-centred medication reviews for people taking multiple medicines in primary care, hospital and outpatient clinics. The reviews were conducted by embedding a single approach for polypharmacy management, building on key recommendations from SIMPATHY. Results: Interventions made were graded, with 82% being classified as clinically significant and 4% potentially preventing major organ failure, adverse drug reactions or incidents of similar clinical importance. The average number of medications reduced from 12 to 11, with 92% of the reviews resulting in more appropriate medication use, thereby decreasing the likelihood of medication-related harm. Inappropriate medicines were stopped, reduced or altered to improve appropriateness. There were significant healthcare resource utilisation benefits as indicated by a positive return on investment for both medication and healthcare costs with a quality-adjusted life year gain of 7.4 per 100 patients. Conclusion: Pharmacist-led, person-centred medication review using the 7-Steps approach was delivered across jurisdictions and healthcare settings, with positive impacts on the number and appropriateness of medicines, clinical interventions and cost savings outweighing expenditure on the service. The approach is scalable by means of the tools and resources developed over the duration of the project
OculusNet: Detection of retinal diseases using a tailored web-deployed neural network and saliency maps for explainable AI
Get PDFRetinal diseases are among the leading causes of blindness worldwide, requiring early detection for effective treatment. Manual interpretation of ophthalmic imaging, such as optical coherence tomography (OCT), is traditionally time-consuming, prone to inconsistencies, and requires specialized expertise in ophthalmology. This study introduces OculusNet, an efficient and explainable deep learning (DL) approach for detecting retinal diseases using OCT images. The proposed method is specifically tailored for complex medical image patterns in OCTs to identify retinal disorders, such as choroidal neovascularization (CNV), diabetic macular edema (DME), and age-related macular degeneration characterized by drusen. The model benefits from Saliency Map visualization, an Explainable AI (XAI) technique, to interpret and explain how it reaches conclusions when identifying retinal disorders. Furthermore, the proposed model is deployed on a web page, allowing users to upload retinal OCT images and receive instant detection results. This deployment demonstrates significant potential for integration into ophthalmic departments, enhancing diagnostic accuracy and efficiency. In addition, to ensure an equitable comparison, a transfer learning approach has been applied to four pre-trained models: VGG19, MobileNetV2, VGG16, and DenseNet-121. Extensive evaluation reveals that the proposed OculusNet model achieves a test accuracy of 95.48% and a validation accuracy of 98.59%, outperforming all other models in comparison. Moreover, to assess the proposed model's reliability and generalizability, the Matthews Correlation Coefficient and Cohen's Kappa Coefficient have been computed, validating that the model can be applied in practical clinical settings to unseen data
Navigating ethical challenges in generative AI-enhanced research: The ETHICAL framework for responsible generative AI use
Get PDFThe rapid adoption of generative artificial intelligence (GenAI) in research presents both opportunities and ethical challenges that should be carefully navigated. Although GenAI tools can enhance research efficiency by automating tasks such as literature reviews and data analysis, their use raises concerns about aspects including data accuracy, privacy, bias, and research integrity. This paper proposes the ETHICAL framework, which is a practical guide for responsible GenAI use in research. Employing a multi-stage single case study design, we examine multiple GenAI tools in real research contexts to develop the ETHICAL framework, which consists of seven key principles: Examine policies and guidelines, Think about social impacts, Harness understanding of the technology, Indicate use, Critically engage with outputs, Access secure versions, and Look at user agreements. Applying these principles will enable researchers to uphold research integrity while leveraging the benefits of GenAI. The framework addresses a critical gap between awareness of ethical issues and practical action steps, providing researchers with concrete guidance for ethical GenAI integration. This work has implications for research practice, institutional policy development, and the broader academic community as researchers adapt to an AI-enhanced research landscape. The ETHICAL framework can also serve as a foundation for developing AI literacy in academia and promoting responsible GenAI adoption in research settings
How autonomous bus trials affect passengers’ views: Exploring the gap between pre-ride expectations and real word experience
Get PDFThe study investigates passengers’ perceptions of a real-world trial involving a level 4 full-sized Automated Bus (AB) operating as a commercial service on a 22 km inter-urban route along public roads in East Scotland. By focusing on a trial, where the AB navigates mixed traffic, different road types (including motorways) and operates at speeds of up to 80 km/h, this research fills a significant gap in the existing literature, which has offered limited exploration of passenger AB experiences in such complex and realistic operational environments. The contribution of this study lies in providing a comprehensive analysis of passenger expectations and satisfaction, considering both the automated driving technology and the service in all its aspects, while also taking into account their interactions. Results (n = 490) revealed generally positive views from passengers with 61.7 % indicating that the AB technology exceeded their expectations and 71.1 % expressing a high likelihood of recommending the service to others. A binary probit model with random parameters showed satisfaction with ride smoothness and vehicle noise, low pre-ride expectations, and a willingness to use unstaffed ABs were key determinants of post-trial evaluation. In addition, frequency of bus use and gender were found to have mixed effects. A second binary probit model found that high pre-trial expectations, infrequent car use, and frequent bus use influenced the net promoter score, with satisfaction with AB driving style and with service characteristics having heterogeneous effects. These findings offer valuable insights for the transport industry, guiding their future developments and strategies for the successful implementation of AB technology
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