6277 research outputs found
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Technological capabilities and supply chain value creation: exploring the roles of circular economy practices and organizational climate
No full textThis study draws on the dynamic capability theory complemented by the resource-based view to examine how technological capability drives supply chain value creation via circular economy practices and when this effect is enhanced by organizational climate. We test our model using survey data from 310 manufacturing SMEs in Ghana. The results demonstrated that circular economy practices fully mediate the relationship between technological capability and supply chain value creation. Also, organizational climate moderates the indirect relationship between technological capabilities and supply chain value creation through circular economy practices. The findings offer key theoretical and managerial implications for scholars and practitioners to enhance value creation
Stroke detection in brain CT images using convolutional neural networks: model development, optimization and interpretability
Full text linkStroke detection using medical imaging plays a crucial role in early diagnosis and treatment planning. In this study, we propose a Convolutional Neural Network (CNN)-based model for detecting strokes from brain Computed Tomography (CT) images. The model is trained on a dataset consisting of 2501 images, including both normal and stroke cases, and employs a series of preprocessing steps, including resizing, normalization, data augmentation, and splitting into training, validation, and test sets. The CNN architecture comprises three convolutional blocks followed by dense layers optimized through hyperparameter tuning to maximize performance. Our model achieved a validation accuracy of 97.2%, with precision and recall values of 96%, demonstrating high efficacy in stroke classification. Additionally, interpretability techniques such as Local Interpretable Model-agnostic Explanations (LIME), occlusion sensitivity, and saliency maps were used to visualize the model’s decision-making process, enhancing transparency and trust for clinical use. The results suggest that deep learning models, particularly CNNs, can provide valuable support for medical professionals in detecting strokes, offering both high performance and interpretability. The model demonstrates moderate generalizability, achieving 89.73% accuracy on an external, patient-independent dataset of 9900 CT images, underscoring the need for further optimization in diverse clinical settings
Informal sport and physical activity in Trinidad and Tobago: sweats as sites of local interaction—and modes of integration for Venezuelan immigrants
No full textThe subject of informal sport and physical activity has often been overlooked within sport scholarship, including research on sport-for-development. This is largely because participation, programmes and analysis usually focus on organised engagement in mainstream sports. However, in some contexts culturally significant social collectives have emerged, involving less formal forms of sporting and physical activities. This chapter examines an example in Trinidad and Tobago, where the sweat (meaning to sweat) is a label used by participants to describe forms of prearranged but unorganised activities. These localised collectives can serve as sites of community cohesion, interpersonal negotiation and societal reconciliation. The unique modes of communication in these spaces (referred to as ‘ole talk’) are integral to how participants interact. A significant number of displaced Venezuelans have relocated to Trinidad and Tobago, and the sweats can also provide important opportunities for members of immigrant communities to negotiate integration into Trinbagonian society. Applying Paolo Freire’s pedagogical notions of problem-posing dialogue, this ethnographic chapter examines this social phenomenon and the dialogue of its participants. It then examines the agency these sites can facilitate, in contrast to the more restrictive modes of participation common within structured sport. The work also explores the facilitation of local interaction and cross-cultural integration of Venezuelan immigrants in these spaces. This chapter adds to the growing scholarship on informal sport and physical activity, examined here as sites of social cohesion and applications of sport-for-development
A structural causal model ontology approach for knowledge discovery in educational admission databases
Full text linkEducational admission systems, particularly in developing countries, often suffer from opaque decision processes, unstructured data, and limited analytic insight. This study proposes a novel methodology that integrates structural causal models (SCMs), ontological modeling, and machine learning to uncover and apply interpretable knowledge from an admission database. Using a dataset of 12,043 records from Benue State Polytechnic, Nigeria, we demonstrate this approach as a proof of concept by constructing a domain-specific SCM ontology, validate it using conditional independence testing (CIT), and extract features for predictive modeling. Five classifiers, Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) were evaluated using stratified 10-fold cross-validation. SVM and KNN achieved the highest classification accuracy (92%), with precision and recall scores exceeding 95% and 100%, respectively. Feature importance analysis revealed ‘mode of entry’ and ‘current qualification’ as key causal factors influencing admission decisions. This framework provides a reproducible pipeline that combines semantic representation and empirical validation, offering actionable insights for institutional decision-makers. Comparative benchmarking, ethical considerations, and model calibration are integrated to enhance methodological transparency. Limitations, including reliance on single-institution data, are acknowledged, and directions for generalizability and explainable AI are proposed
"What we know is a drop”: contextualising Netflix’s Dark
No full textNetflix’s Dark (2017–2020) is a landmark in international television and Netflix’s first German-language original series. Set in Winden, the show intertwines four families across generations and parallel worlds, using time travel to explore trauma, grief, free will, and intergenerational guilt. This edited volume contextualizes Dark’s narrative complexity, philosophical depth, aesthetic strategies, and cultural resonance, including its engaged global fandom. Organized into four thematic sections, the collection highlights the series as storytelling, cultural critique, and philosophical inquiry, demonstrating its enduring relevance and status as a defining work of twenty-first-century global television
Social network analysis in football: a systematic review of performance and tactical applications
Full text linkIntroduction: This systematic review aims to critically examine the application of social network analysis (SNA) in football, with a focus on its contribution to evaluating team performance, tactical behavior, and player interactions. Methods: Following PRISMA guidelines, a comprehensive search was conducted across four databases (PubMed, Scopus, Web of Science, and SPORTDiscus) from January 2017 to October 2024. Results: Fifty-five peer-reviewed studies met the inclusion criteria, addressing network analysis in official men's professional football matches. Data were extracted and summarized regarding methodological quality, network metrics used, tactical context, and practical implications. Discussion: Most studies demonstrated that cohesive network structures, characterized by high density, clustering coefficients, and centrality, are associated with successful team performance. Centrality metrics were frequently used to identify key tactical players, typically central defenders and midfielders. Recent methodological advances included dynamic time-window analysis, pitch-passing networks, and spatial-temporal integration using tracking data. However, there remains an overrepresentation of elite men's football and offensive phases, with limited focus on defensive networks, youth categories, and women's football. SNA offers a powerful framework to decode the complexity of football performance, evolving from static graphs to dynamic, rolesensitive, and context-rich models. Future research should adopt longitudinal designs, multi-layer network approaches, and closer collaboration with practitioners to enhance the operational utility of network insights in coaching and performance analysis. Systematic review registration: https://osf.io/2pe3
Enhancing maritime education for digital sustainability
Full text linkPurpose Digital sustainability involves the ability of industries and professionals to adapt to rapidly changing technological landscapes. Digitalisation and artificial intelligence (AI) are expected to radically change the maritime industry’s job landscape, especially with autonomous ships. International organisations currently do not formalise the education of maritime professionals and deck officers and need new formal modules. This study aims to contribute to this aspect by investigating learners’ experiences and knowledge gaps in the fundamentals of, as supported by the andragogy theory, topics such as computer programming, cybersecurity and statistics. Design/methodology/approach The research was carried out at Southampton Solent University, with samples of 105 students attending various MSc courses in maritime operations and deck cadet courses. The data was collected through an online survey. The two groups were compared and analysed using a chi-square test. Findings The results show that the percentage of MSc students with previous training in statistics, computer programming and cybersecurity courses was 37%, 13% and 16%, respectively. The deck officers’ training in the same areas was 06%, 09% and 09%. The results of this study were used to develop a new maritime digital module to focus on these topics. Originality/valueThe paper highlights digital sustainability’s significance in adapting education and training courses. Ship management companies and higher education institutions must urgently meet the demands of digitalisation and AI in the maritime industry. It highlights the necessity of addressing current knowledge gaps and implementing new educational modules to ensure the sustainable development of digital skills among maritime professionals and cadets
Cybersecurity threats of remote autonomous ships while approaching ports
No full textThe utilisation of new information technologies is essential for the design of remote-controlled ships. However, cyber threats pose significant risks to both their safety and security. Since 2021, international regulatory innovation has focused on the cyber hygiene of ships, focusing on ship procedures and training. This study investigates the potential exploitation of remote-controlled ships during port navigation. Therefore, this study proposes a risk-based methodology to evaluate ship cybersecurity threats within port limits. Leveraging STPA-SafeSec’s analysis, security threats are identified. FAHP is employed to assess the severity of each constraint. The study focuses on UK ports. Twenty-one cases in the UK are used to weigh the severity of an accident caused by a ship in port. The study highlights the importance of port facilities in monitoring cyber threats for ships. Overexposure to web ship information, combined with the proximity of UK ports in cities, is found to be a severe threat
Knowledge and associated factors of obstetric danger signs among pregnant women in northern Ghana: a health facility based cross-sectional study
No full textObjective: Assessing knowledge and associated factors on obstetric danger signs among pregnant women attending antenatal care at Tumu Government Hospital.Design: A descriptive cross-sectional design adopted recruited 399 participants through a simple random sampling technique into the study. A structured questionnaire was developed to collect data one on one with participants. Study data were analysed descriptively and inferentially using SPSS (27) and a probability value of <0.05 indicated a significant association between the dependent and independent variablesSubjects: Pregnant women attending antenatal careOutcome Measure: Obstetric Danger SignsResults: About 17% of pregnant women had poor knowledge of obstetric danger signs. Participants occupation [p=0.001], first trimester [p=0.012], Second trimester [p=0.001], Multigravida [p=0.006] and Previous skilled birth [p=0.0001] significantly predicted poor knowledge on obstetric danger signs.Conclusion: Pregnant women had poor knowledge on obstetric danger signs. Awareness of pregnant women through intensive health education programs would help avert the complications associated with obstetric complications. Further study is recommended to examine the role of traditional practices in the emergence of obstetric danger signs among pregnant women
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