Search Results (516)

Background/Objectives: Dementia is a leading cause of cognitive decline, with significant challenges for early detection and timely intervention. The lack of effective, user-centred technologies further limits clinical response, particularly in underserved areas. This study aimed to develop and describe a co-design process for creating a Diagnostic and Statistical Manual of Mental Disorders (DSM-5)-compliant, AI-powered Smart Assistant (SmartApp) to monitor neurocognitive decline, while ensuring accessibility, clinical relevance, and responsible AI integration. Methods: A co-design framework was applied using a novel combination of Agile principles and the Double Diamond Model (DDM). More than twenty iterative Scrum sprints were conducted, involving key stakeholders such as clinicians (psychiatrist, psychologist, physician), designers, students, and academic researchers. Prototype testing and design workshops were organised to gather structured feedback. Feedback was systematically incorporated into subsequent iterations to refine functionality, usability, and clinical applicability. Results: The iterative process resulted in a SmartApp that integrates a DSM-5-based screening tool with 24 items across key cognitive domains. Key features include longitudinal tracking of cognitive performance, comparative visual graphs, predictive analytics using a regression-based machine learning module, and adaptive user interfaces. Workshop participants reported high satisfaction with features such as simplified navigation, notification reminders, and clinician-focused reporting modules. Conclusions: The findings suggest that combining co-design methods with Agile/DDM frameworks provides an effective pathway for developing AI-powered clinical tools as per responsible AI standards. The SmartApp offers a clinically relevant, user-friendly platform for dementia screening and monitoring, with potential to support vulnerable populations through scalable, responsible digital health solutions. Full article

The pandemic spared most children and adolescents in Ghana from severe clinical disease, but it exposed long-standing gaps in services and protection methods. Methods: We conducted a desk-based narrative review of peer-reviewed studies, national and international reports, and grey literature from January 2020 to May 2025. The evidence was organised across eight domains of child and adolescent well-being. Across mental health, gambling and other risky behaviours, access to health services, economic hardship and child labour, nutrition, education, early childhood development, and WASH, the pandemic disrupted essential services and social safety nets. Examples include declines in routine care and immunisation, wider digital exclusion during remote learning, a rise in child labour linked to income loss, and persistent hygiene constraints. Preparedness in Ghana should focus on mental health, digital inclusion, early childhood services, and strong social protection. Ghana’s specific empirical data are uneven, so we triangulate peer-reviewed evidence with official reports, appraised the grey literature, and calibrated claims to the strength of sources. Full article

Learning has established itself as a fundamental pillar for the adaptation and continuous growth of organisations. This article analyses the impact of organisational learning on innovation and institutional performance in universities, focusing on five interdependent dimensions: organisational culture, knowledge management, organisational memory, continuous feedback, and dynamic capabilities. Through a narrative review of the specialised literature, a systemic framework is proposed that conceives organisational learning as an integral and strategic process, where each dimension contributes in key ways to institutional strengthening. Organisational culture fosters shared values and readiness for change; knowledge management enables the generation and application of relevant knowledge; organisational memory guarantees the continuity and transfer of learning; constant feedback facilitates adaptation; and dynamic capabilities prepare the university to face complex and changing contexts. As a practical contribution, an operational agenda is designed that links each dimension with a strategic action, a follow-up indicator, a suggested institutional tool, and theoretical references. This proposal seeks to offer an adaptable roadmap for management teams, quality assurance units, and university management training spaces. Full article

This study seeks to identify the factors influencing the implementation of Project Management Methodologies (PMM) in Local Government (LG) and identify the concepts, themes and characteristics that make up each of those factors. Semi-structured interviews were employed as the primary technique, engaging practitioners directly involved in local government capital works projects. This approach allowed for flexibility in exploring individual perspectives while maintaining consistency across key thematic areas. The interviews were designed to elicit rich, detailed narratives about organisational practices, procedural challenges, and behavioural attitudes toward PMM. Subsequently, a qualitative thematic analysis was adopted for the study. Through systematically coding, insights emerge regarding the key factors influencing PMM adoption, deployment, and optimisation. The findings suggest that strong leadership commitment, adaptive learning and structured oversight are critical for successful PMM implementation. “Governance”, “Experience and competency” and “Comparison and reflection” appear to be the most influential factors for PMM adoption, deployment and optimisation, respectively. The outcomes of this research will assist LGs in identifying and understanding the factors that influence the implementation of a PMM. Currently, no mandatory national policies standardise project management capabilities within the LG sector in Australia. Therefore, the outcomes of this study will provide a substantial body of knowledge and a platform to identity, analyse and evaluate the factors influencing the implementation of a PMM to the existing management practices within LGs. Full article

Background/Objectives: The concept of learning organisations, which rely on research-based evidence and emphasise employees’ continuous learning and professional development, is essential for effectively managing change and enhancing the quality of services. To better understand the effects of implementing learning organisation principles in hospital settings, this study aimed to compare the perceptions of selected health professionals regarding learning organisations across five sub-districts in South Africa’s Eastern Cape province. Methods: This study employed a quantitative, cross-sectional survey design to collect data from selected health professionals in 9 hospitals in Eastern Cape province. Results: Overall, the study showed wide variations in how different healthcare professionals perceived the commitment and communication of department leaders regarding the importance of learning. Key findings showed that 90.9% of dentists agreed that department leaders communicate the importance of learning. In comparison, 68.8% of allied health professionals and 56.5% of pharmacists agreed that department leaders communicated the importance of learning; 21.7% of pharmacists and 25% of allied health professionals disagreed. Conclusions: The study has highlighted significant disparities in the perceptions of selected hospital-based health professionals toward introducing a learning organisation in healthcare facilities. This divergence points to the need for tailored approaches in communicating and implementing LO strategies within hospital settings to ensure that all health professionals are equally engaged and that the benefits of continuous learning are fully realised across disciplines. Full article

Neurocutaneous syndromes, known as phakomatoses, encompass a diverse group of congenital conditions affecting the nervous system and skin, with neurofibromatosis type 1 (NF1) and neurofibromatosis type 2 (NF2) among the most clinically significant. Both disorders are inherited in an autosomal dominant manner. NF1 presents with café-au-lait macules; cutaneous, subcutaneous, and plexiform neurofibromas; skeletal abnormalities; learning disabilities; and optic pathway gliomas, while NF2 is characterised by bilateral vestibular schwannomas, multiple meningiomas, ependymomas, and peripheral nerve schwannomas. Although germline mutations in the NF1 and NF2 tumour suppressor genes are well established, they do not fully explain the broad clinical variability observed, even among individuals carrying identical mutations. As increasingly recognised in other genetic diseases, epigenetic mechanisms, including DNA methylation, histone modifications, chromatin remodelling, and non-coding RNA (ncRNA) regulation, play a critical role in modulating gene expression and influencing disease severity. Despite important findings, the research remains fragmented, and a unified model is lacking. This review organises the current knowledge, emphasising how epigenetic alterations impact disease behaviour and outlining their potential as prognostic biomarkers and therapeutic targets. A deeper understanding of these mechanisms could lead to improved personalised management and the development of targeted epigenetic therapies for individuals with NF1 and NF2. Full article

There is an increasing demand for robust methodologies to rigorously evaluate the psychometric properties of measurement scales used in quantitative research across various scientific disciplines. This article proposes an integrative method that combines structural equation modelling (SEM) with machine learning (ML) to jointly assess model fit and predictive accuracy, limitations often addressed separately in traditional approaches. Using a measurement scale for voluntary employee turnover intention, the method demonstrates clear improvements: RMSEA decreased from 0.073 to 0.065, and classifier accuracy slightly increased from 0.862 to 0.863 after removing three redundant items. Compared to standalone SEM or ML, the integrated framework yields a shorter, better-fitting scale without compromising predictive power. For practitioners, this method enables the creation of more efficient, theoretically grounded, and predictive tools, facilitating faster and more accurate assessments in organisational settings. To this end, this study employs Covariance-Based SEM (CB-SEM) in conjunction with classifiers such as naive Bayes, linear and nonlinear support vector machines, decision trees, k-nearest neighbours, and logistic regression. Full article

This systematic review explores the number and role of the UNO^® card game and UNO-type learning media across different levels of mathematics education. UNO^®-inspired games have gained recognition as innovative pedagogical tools that promote engagement, motivation, and active learning among students in various mathematics topics in the last few years. This review synthesises existing research on the application of these games in preschool, primary, secondary, and higher education settings. We employed a hybrid approach, combining database searches and the snowball method, to ensure a comprehensive and thorough selection of the relevant literature for our systematic review. Published research studies between January 1980 and June 2025 that employed UNO^® or UNO-type card games in education were collected. Forty-one research studies met the inclusion criteria for this review. We sought answers to three research questions. At first, we made a time analysis to organise the collected records. Based on the collection, we identified the mathematical topics for which teachers and researchers used the original UNO^® game or developed UNO-type games at various educational levels, as well as the role of UNO^® and UNO-type games in mathematics education. In approximately 68.29% of the cases, the authors introduce their own educational games modelled after UNO^®. In contrast, just less than one-third of the records (31.71%) utilise the original UNO^® playing cards in the teaching–learning process. The findings indicate that UNO-type educational maths games can enhance conceptual understanding, foster collaborative skills, and improve learning outcomes when appropriately integrated into curricula. This review also offers educators recommendations for effectively introducing these games at various educational levels. We aim to provide an evidence-based, well-structured insight into the potential of UNO^® and UNO-type learning tools. Full article

Nature-based interventions are increasingly used to support human wellbeing, and more recently to integrate migrants into host countries. In this research, we focused on a nature-based vocational training programme led by a partnership of three Swedish public organisations. In the programme, long-term unemployed Swedes worked with migrants in various nature conservation and nature management tasks as part of an employment programme. We undertook interviews with nineteen participants and carried out observations ‘in situ’ to explore the impact of the programme on participants’ wellbeing. Using the ‘five ways to wellbeing’ as a conceptual framework, we found that the programme connected participants to nature, helped them take notice of the forests and nature they worked in, and connected participants across cultures. The participants learned new conservation skills and migrants had a chance to improve their Swedish language skills. The activities within the programme were physical and the majority found this was important for their overall wellbeing. Migrants were younger and keener to learn new employable skills than Swedes who were closer to pension age. The groups were more similar when it came to identifying the impact of the programme on their wellbeing. Nature-based vocational training programmes not only allow participants to gain skills for employment but can have a significant impact on wellbeing. Full article

Neural networks (NNs) achieve breakthroughs in computer vision and natural language processing, yet their “black box” nature persists. Traditional methods prioritise parameter optimisation and loss design, overlooking NNs’ fundamental structure as topologically organised nonlinear computational systems. This work proposes a complex network theory framework decoding structure–function coupling by mapping convolutional layers, fully connected layers, and Dropout modules into graph representations. To overcome limitations of heuristic compression techniques, we develop a topology-sensitive adaptive pruning algorithm that evaluates critical paths via node strength centrality, preserving structural–functional integrity. On CIFAR-10, our method achieves 55.5% parameter reduction with only 7.8% accuracy degradation—significantly outperforming traditional approaches. Crucially, retrained pruned networks exceed original model accuracy by up to 2.63%, demonstrating that topology optimisation unlocks latent model potential. This research establishes a paradigm shift from empirical to topologically rationalised neural architecture design, providing theoretical foundations for deep learning optimisation dynamics. Full article

Prescriptive Maintenance (PsM) transforms industrial asset management by enabling autonomous decisions through simultaneous failure anticipation and optimal maintenance recommendations. Yet, despite increasing research interest, the conceptual clarity, technological maturity, and practical deployment of PsM remains fragmented. Here, we conduct a comprehensive and application-oriented Systematic Literature Review of studies published between 2013–2024. We identify key enablers—artificial intelligence and machine learning, horizontal and vertical integration, and deep reinforcement learning—that map the functional space of PsM across industrial sectors. The results from our multivariate meta-synthesis uncover three main thematic research clusters, ranging from decision-automation of technical (multi)component-level systems to strategic and organisational-support strategies. Notably, while predictive models are widely adopted, the translation of these capabilities to PsM remains limited. Primary reasons include semantic interoperability, real-time optimisation, and deployment scalability. As a response, a structured research agenda is proposed to emphasise hybrid architectures, context-aware prescription mechanisms, and alignment with Industry 5.0 principles of human-centricity, resilience, and sustainability. The review establishes a critical foundation for future advances in intelligent, explainable, and action-oriented maintenance systems. Full article

Background: Rock–blast design is a canonical inverse problem that joins elastodynamic partial differential equations (PDEs), fracture mechanics, and stochastic heterogeneity. Objective: Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, a systematic review of mathematical methods for geomechanically informed blast modelling and optimisation is provided. Methods: A Scopus–Web of Science search (2000–2025) retrieved 2415 records; semantic filtering and expert screening reduced the corpus to 97 studies. Topic modelling with Bidirectional Encoder Representations from Transformers Topic (BERTOPIC) and bibliometrics organised them into (i) finite-element and finite–discrete element simulations, including arbitrary Lagrangian–Eulerian (ALE) formulations; (ii) geomechanics-enhanced empirical laws; and (iii) machine-learning surrogates and multi-objective optimisers. Results: High-fidelity simulations delimit blast-induced damage with ≤0.2 m mean absolute error; extensions of the Kuznetsov–Ram equation cut median-size mean absolute percentage error (MAPE) from 27% to 15%; Gaussian-process and ensemble learners reach a coefficient of determination ($R^2>0.95$) while providing closed-form uncertainty; Pareto optimisers lower peak particle velocity (PPV) by up to 48% without productivity loss. Synthesis: Four themes emerge—surrogate-assisted PDE-constrained optimisation, probabilistic domain adaptation, Bayesian model fusion for digital-twin updating, and entropy-based energy metrics. Conclusions: Persisting challenges in scalable uncertainty quantification, coupled discrete–continuous fracture solvers, and rigorous fusion of physics-informed and data-driven models position blast design as a fertile test bed for advances in applied mathematics, numerical analysis, and machine-learning theory. Full article

The rubber industry is facing major socio-economic and environmental constraints. Rubber-based agroforestry systems represent a more sustainable solution through the diversification of income and the provision of greater ecosystem services than monoculture plantations. Participative approaches are known for their ability to co-construct solutions with stakeholders and to promote a positive impact on smallholders. This study therefore implemented a participatory research process with stakeholders in the natural rubber sector for the purpose of improving inclusion, relevance and impact. Facilitation training sessions were first organised with academic actors to prepare participatory workshops. A working group of stakeholder representatives was set up and participated in these workshops to share a common representation of the value chain and to identify problems and solutions for the sector in Indonesia. By fostering collective intelligence and systems thinking, the process is aimed at enabling the development of adaptive technical solutions and building capacity across the sector for future government replanting programmes. The resulting adaptive technical packages were then detailed and objectified by the academic consortium and are part of a participatory plant breeding approach adapted to the natural rubber industry. On-station and on-farm experimental plans have been set up to facilitate the drafting of projects for setting up field trials based on these outcomes. Research played a dual role as both knowledge provider and facilitator, guiding a co-learning process rooted in social inclusion, equity and ecological resilience. The initiative highlighted the potential of rubber cultivation to contribute to climate change mitigation and food sovereignty, provided that it can adapt through sustainable practices like agroforestry. Continued political and financial support is essential to sustain and scale these innovations. Full article

Bring them home, keep them home is research based in New South Wales (NSW) Australia, that aims to understand successful and sustainable reunification for Aboriginal families who have children in out-of-home care (OOHC). This research is led by Aboriginal researchers, and partners with Aboriginal organisations. It is informed by the experiences of 20 Aboriginal parents and family members, and more than 200 practitioners and professionals working in child protection and reunification. This paper traces the evolution of Bring them home, keep them home which is now at the forefront of influence for NSW child protection reforms. Using specific examples, it highlights the role of research advocacy and resistance in challenging and disrupting systems in ways that amplify the voices of Aboriginal families and communities and embeds these voices as the foundation for radical innovation for child reunification approaches. The paper shares lessons being learned and insights for Aboriginal-led research with communities in the pursuit of restorative justice, system change, and self-determination. Providing a framework for liberating Aboriginal families from child abduction systems, this paper seeks to offer a truth-telling and practical contribution to the international efforts of Indigenous resistance to child abduction systems. Full article

Advanced technologies, such as robotic process automation, blockchain, and machine learning, increase audit efficiency. Nonetheless, some Supreme Audit Institutions (SAIs) have not undergone digital transformation. This research aimed to develop a comprehensive framework for supreme audit institutions to adopt and integrate emerging technologies into their auditing processes using a hybrid theoretical approach based on the TAM (Technology Acceptance Model) and TOE (Technology–Organisation–Environment) models. The framework was informed by insights from nineteen highly experienced experts in the field from eight countries. Through a two-round Delphi questionnaire, the experts provided valuable input on the key factors, challenges, and strategies for successful technology adoption by public sector audit organisations. The findings of this research reveal that technology adoption in SAIs starts with solid management support led by the chief technology officer. They must evaluate the IT infrastructure and readiness for advanced technologies, considering the budget and funding. Integrating solutions like the SAI of Ghana’s Audit Management Information System can significantly enhance audit efficiency. Continuous staff training is essential to build a positive attitude toward new technologies, covering areas like data algorithm auditing and big data analysis. Assessing the complexity and compatibility of new technologies ensures ease of use and cost-effectiveness. Continuous support from technology providers and monitoring advancements will keep SAIs aligned with technological developments, enhancing their auditing capabilities. Full article