York St John University

Flood hydrology sits at the interface of science, public protection, infrastructure planning, and regulation. It is a broad and interdisciplinary field; in a recent UK survey of users of hydrology only 45 % self-identified as a hydrologist. To ensure society is prepared for increasing hydrological risks, effective communication within this diverse community is essential. Without clear pathways for translation pathway between policy priorities, emerging research and operational needs, critical planning and policy decisions risk being made on outdated assumptions. However, operational decisions are not always able to draw upon the latest research into process understanding or modelling approaches due to multiple barriers. These barriers include uneven access to data and tools, capacity constraints, differences in incentives across sectors and the limited time for relationship building and knowledge translation across different expertise. Here, we present insights from around 60 participants at a “science into practice” workshop hosted at the British Hydrological Society Symposium (University of Oxford, September 2024). The workshop was designed as a sector-spanning exercise between researchers, consultants, regulators, and practioners working on flood hydrology across the UK. Across sectors, participants converged on six priority themes: (1) working together, (2) funding and responsibilities, (3) skills and training, (4) data, (5) methods, and (6) accreditation and usability. We reflect on how these themes provided insights into the challenges and opportunities for science communication, knowledge translation and collaboration, and why such activities are often undervalued despite their critical role for improving flood-risk decisions. We conclude with practical recommendations for improving “science into practice” pathways in flood hydrology with more inclusive cross-sector communication aligned with the goals of the co-developed 25-year UK Flood Hydrology Roadmap. These lessons learned are transferable to other areas of environmental risk where effective communication and collaboration are crucial for delivering societal and environmental benefits.

ABSTRACT Generative artificial intelligence (GenAI) is increasingly conceptualised within social entrepreneurship as a socio‐technical resource embedded in collaborative ecosystems. Empirical and theoretical developments remain fragmented, particularly in explaining how ecosystem configurations shape socio‐technical innovation beyond isolated tool adoption. This study synthesises peer‐reviewed scholarship through a bibliometric–systematic literature review to map the intellectual structure and thematic evolution of the field. The following four research streams are identified: design and knowledge creation, stakeholder engagement, ethics and accountability, as well a sustainable entrepreneurship. Integrating institutional, governance, capability‐based and open innovation perspectives, the study develops an ecosystem‐level framework that articulates boundary conditions under which GenAI is associated with collaborative value creation. Three analytically derived mechanisms are specified for future empirical inquiry: algorithmic legitimacy, relational dynamic capabilities and distributed sustainability capability. The review highlights enduring gaps, including limited treatment of participatory oversight, power asymmetries and digitally marginalised contexts. By advancing a theoretically integrated and mechanism‐oriented account, the study contributes to cumulative research on GenAI‐enabled social innovation and provides a structured basis for context‐sensitive governance deliberation.

The aim of the study was to identify central determinants of using a 3-year longitudinal evaluation of left ventricular (LV) morphological and functional (global, tissue-Doppler and strain) outcome measures obtained at rest and during both submaximal and maximal exercise in a group of highly trained male youth soccer players (SP) and recreationally active male participants (CON). Once a year for 3years, measurements were obtained in both the SP and CON groups (12.0±0.3 and 11.7±0.2years of age, respectively, at the onset of the study). Cardiac ultrasound measures were used to identify LV morphological indices at rest and functional parameters during submaximal and maximal exercise. Training status (P<0.0001) emerged as the only significant independent predictor of , when considering LV morphological variables. At maximal exercise, early diastolic filling (E) was a significant (P=0.001) predictor of , irrespective of the influence of training status. Training status emerged as the significant predictor of across all models that were developed in this study. Minimal LV structural and functional adaptations at both rest and exercise influence , beyond the impact of training status alone. The broader implication of these findings is that the influence of LV cardiac adaptations on over time is mediated by the stimulus of training; this association occurs independently from the impact of growth and maturation on .

ABSTRACT Background Without a cure for dementia, providing psychosocial support to people living with the condition is imperative. There is a developing evidence base for psychological and therapeutic support for people living with dementia. However, there is little to guide therapists working in this area, in terms of approaches and techniques that are likely to be successful. Methods In the present study, we utilised different methods to engage people with dementia and their family members in discussions around ways in which psychotherapists could improve the delivery and accessibility of person‐centred therapy, to develop core competences for therapists. We brought together evidence from interviews with people with dementia, their families, and psychotherapists, with workshops utilising a World Café approach. Findings We hosted four workshops with 47 participants in total. Using thematic analysis, we identified core themes across the different forms of data. We developed core competences in four areas: knowledge of dementia, therapeutic techniques and skills, therapeutic relationship and scope of practice. Discussion The competences developed present an overview of expectations for those working therapeutically with families affected by dementia. It is hoped that their development leads to increased awareness of the specialist skills needed to engage with people with dementia, valuing of these skills for psychotherapists.

Abstract The integration of artificial intelligence (AI) and machine learning (ML) in waste management has the potential to significantly advance circular economy objectives by enhancing efficiency, reducing waste, and optimizing resource recovery. However, realising these benefits depends on addressing significant technical, economic, and systemic barriers. AI technologies, such as intelligent waste‐sorting systems and predictive models, are transforming how waste is processed and materials are reused. This article critically evaluates the potential and limitations of AI‐driven approaches across the waste management lifecycle through a narrative review of peer‐reviewed literature published between 2015 and 2025. AI offers a revolutionary approach to waste management, resource recovery, and environmental impact reduction by enabling the processing of massive datasets and automating complex decision‐making. However, to fully realize AI's promise, critical issues, including scarce data availability, expensive implementation costs, the requirement for efficient human‐AI cooperation, and ethical considerations regarding algorithmic transparency and workforce impacts, must be systematically addressed. Additionally, ethical concerns related to job displacement and the environmental footprint of AI technologies themselves require careful management. This review identifies significant research gaps, including the need for standardized datasets, explainable AI frameworks, and comprehensive lifecycle assessments of AI‐driven interventions. Looking to the future, decentralized AI systems, AI‐driven global waste trade optimization, blockchain‐integrated tracking systems, and AI‐enhanced product design offer promising avenues for further innovation. As AI continues to develop, its incorporation into waste management systems will be essential to accelerating the world's shift to a circular economy that is more resource‐efficient and sustainable.

The increasing adoption of cloud computing within the banking and financial industry has introduced significant challenges related to secure authentication, authorization, and identity management, particularly in highly regulated environments such as Sri Lanka. This paper explores the application of cloud-based authentication and access control mechanisms using Microsoft Azure to address these challenges. Cloud-native architecture is proposed that integrates Azure Kubernetes Service, Azure Functions, Azure API Management, and Azure Active Directory B2C to deliver a scalable, resilient, and secure user authentication platform. The proposed solution addresses critical identity and access management concerns, including session management, account lockout enforcement, password recovery workflows, user registration and login processes, auditing and logging, role-based access control (RBAC), multi-factor authentication (MFA), and the protection of secure API endpoints. The architecture is designed to meet industry requirements for secure account provisioning, flexible access control policies, and robust protection of sensitive financial data. Empirical findings demonstrate the system's ability to integrate effectively with heterogeneous and legacy IT environments commonly found in financial institutions, while simplifying identity and access management (IAM) operations and supporting regulatory compliance. Despite these advantages, several practical limitations are identified, including architectural complexity, continuous monitoring and maintenance requirements, usability constraints, and the dynamic nature of regulatory and compliance frameworks. The paper further outlines future enhancement opportunities, such as the incorporation of advanced threat detection techniques, adaptive and risk-based MFA mechanisms, and more mature cloud-native security models. Overall, the proposed solution provides practical value to financial institutions by mitigating authentication and authorization weaknesses in cloud environments, offering an efficient and scalable identity service platform that enhances operational efficiency, regulatory compliance, and cybersecurity resilience in the face of evolving digital threats.

Purpose This paper presents the Farm Business Assessment Framework (FBAF), a research tool designed for academic researchers to understand entrepreneurial farming contexts. It responds to the limitations of generic business and management models, which often fail to capture the nuances of farming and rural contexts as well as the distinctive institutional, spatial, cultural and temporal factors that shape them. Design/methodology/approach The FBAF advances McElwee and Smith’s (2012) Farmer Segmentation Framework (FSF) by incorporating Bronfenbrenner’s Ecological Systems Theory (EST) to capture wider contextual influences. It comprises five interrelated contextual layers – individual, microsystem, mesosystem, macrosystem and chronosystem – linking personal, business, societal and institutional dimensions. The framework is informed by the authors’ insider positionalities within the agricultural industry, alongside prior ethnographic research that utilised the FSF. Findings The FBAF theorises the main influences on farming contexts across different levels, illustrating how they generate both opportunities and constraints that shape farm development strategies. It makes three contributions. First, it provides researchers with a unified framework while supporting the development of their own conceptualisations of farmers within their contexts. Second, it serves as a methodological aid, useful for empirical research into farmers’ lived experiences, particularly for scholars unfamiliar with rural and farming environments. Third, it functions as an analytical tool, enabling the construction of farm-based business and management cases. Originality/value The paper introduces a context-sensitive and multi-purpose tool for academic researchers studying farm entrepreneurship. It responds to calls for greater contextualisation in entrepreneurship research and contributes to the emerging body of work on constrained rural entrepreneurship, offering insight into the micro-, meso- and macro-level constraints and opportunities shaping farm enterprises.