6277 research outputs found
Sort by
Factors influencing strength development in young individuals
Get PDFStrength has been gaining special prominence among the conditioning capacities, given its determining influence on motor performance, being present in all movements. In growing youth, the development of strength has gained significant importance, departing from old beliefs that it should not be trained during the childhood and adolescent period. However, in physical education classes, this capacity, along with others, occupies a secondary place in the scheduling of teachers' educational activities. A deficit in strength can lead to multiple demands of school sports activities not being properly met, or their performance not eliciting the desired motivation for the continuation of regular practice. Therefore, the physical education teacher, being privileged in daily contact with young people, should promote a set of stimuli during class that allows all students, regardless of their initial state, to develop the conditional capacity for strength so that they can perform their daily activities more efficiently and with less effort. This increase in strength parameters subsequently allows for a higher success rate in performing different technical movements in various sports, increasing students' motivation for practice, and consequently, their preference for regular physical exercise outside the school context. Thus, the objective of this article is to conduct a review of the contents on the theme of strength development in youth, researched and studied to date by the scientific community
Translating novel collective behaviour measures to concepts and principles of play as understood by football coaches
No full textBackgroundA range of innovative performance analysis metrics have been applied in recent years to investigate aspects of football using tempo-spatial and network analyses. These approaches have gained traction within some professional teams to quantify and assess features of collective behaviour. However, metrics employed are rarely created from, or clearly link to, domain expertise and as a result coaches may be hesitant of their value. Therefore, the aim of this study was to identify coach perceptions of spatial temporal and network metrics and identify the feasibility of an iterative and collaborative process to developing metrics.MethodsTwo rounds of semi-structured interviews were conducted with three Scottish youth international UEFA Pro License coaches (age: 47.0?±?2.7 years) with a focus on aligning metrics with concepts and principles of play. An iterative approach was used centring around spatial-temporal and network metrics and their adaptation. Reflexive thematic analyses were conducted with final metrics categorized as resonant (accurately describing concept or principles of play), relevant (appropriate but with limitations that need improvement), or hesitant (skeptical of usefulness).ResultsAcross the ten recognized principles of play, nine metrics were identified and adapted to varying degrees. Resonant metrics included: network intensity (mobility), distance between defenders (discipline), triangles (support), team length and distance between deepest defender and goal line (depth).ConclusionCoaches recognize principles of play within complex collective behaviour metrics and should be encouraged to collaborate with analysts to develop support systems that may prove to be more valuable and usable
Evaluation of the Omni-Secure Firewall System in a private cloud environment:Knowledge
No full textThis research explores the optimization of firewall systems within private cloud environments, specifically focusing on a 30-day evaluation of the Omni-Secure Firewall. Employing a multi-metric approach, the study introduces an innovative effectiveness metric (E) that amalgamates precision, recall, and redundancy considerations. The evaluation spans various machine learning models, including random forest, support vector machines, neural networks, k-nearest neighbors, decision tree, stochastic gradient descent, naive Bayes, logistic regression, gradient boosting, and AdaBoost. Benchmarking against service level agreement (SLA) metrics showcases the Omni-Secure Firewall’s commendable performance in meeting predefined targets. Noteworthy metrics include acceptable availability, target response time, efficient incident resolution, robust event detection, a low false-positive rate, and zero data-loss incidents, enhancing the system’s reliability and security, as well as user satisfaction. Performance metrics such as prediction latency, CPU usage, and memory consumption further highlight the system’s functionality, efficiency, and scalability within private cloud environments. The introduction of the effectiveness metric (E) provides a holistic assessment based on organizational priorities, considering precision, recall, F1 score, throughput, mitigation time, rule latency, and redundancy. Evaluation across machine learning models reveals variations, with random forest and support vector machines exhibiting notably high accuracy and balanced precision and recall. In conclusion, while the Omni-Secure Firewall System demonstrates potential, inconsistencies across machine learning models underscore the need for optimization. The dynamic nature of private cloud environments necessitates continuous monitoring and adjustment of security systems to fully realize benefits while safeguarding sensitive data and applications. The significance of this study lies in providing insights into optimizing firewall systems for private cloud environments, offering a framework for holistic security assessment and emphasizing the need for robust, reliable firewall systems in the dynamic landscape of private clouds. Study limitations, including the need for real-world validation and exploration of advanced machine learning models, set the stage for future research directions
Evaluation of constraints for investment in NOx emission technologies: case study on Greek bulk carrier owners
No full textPurpose The maritime industry is the transport mode that contributes most to air pollution. The International Maritime Organization (IMO) identified the reduction of air pollution by ships as a crucial issue. Since 1 January 2020, ships have had to adopt strategies and new technologies to eliminate air pollution. However, ship compliance with nitrate oxide (NOx) emission restrictions is more challenging. This paper aims to identify ship owners' challenges in investing in new technologies.Design/methodology/approachThis paper applied a hybrid methodology combining a survey, a balanced scorecard and fuzzy analytic hierarchy process (F-AHP) to identify and evaluate constraints and weights in investment decision-making for NOx technologies. A survey was carried out to validate constraints.Findings A survey was carried out, representing 5.1% of Greek-owned ships by deadweight capacity. The findings provide a weighted list of seven crucial technical and economic constraints faced by ship operators. The constraints vary from ship retrofit expenditure to crew training and waste management. Additionally, NOx emission technologies were compared. It was found that liquefied natural gas is the preferred investment option for the survey participants compared with selective catalytic reduction, exhaust gas recirculation and batteries.Originality/value Several studies have dealt with the individual technical feasibility of NOx reduction technologies. However, apart from technical feasibility for a shipowner, the selection of a NOx technology has several managerial and safety risks. Therefore, the originality of this paper is to reveal those constraints that have a higher weight on ship owners. With this cost-benefit approach, investment challenges for ship operators are revealed. Policymakers can benefit from the results of the employed methodology
Reconfiguring genre, style, and idiolect: investigating progressive rock’s meta-genre and affordances
No full textIn this article, the relationship between the classificatory terms of genre, style, and idiolect is examined. Focusing on progressive rock – noted for its heterogenous nature – I propose that we should adapt our current understanding of idiolect to encompass both the collective idiolects associated with particular bands, and the personal idiolects of musicians that perform in them. Allan F. Moore has previously argued that for some progressive rock bands, their idiolect may transcend the notion of style. In this article, I suggest that the collective idiolect of a band may also transcend genre, and that we should think of progressive rock not only as a set of sub-genres or a network of styles, but also as an assemblage of collective idiolects. Moreover, I contend that greater attention should be paid to the classificatory activities of progressive rock fans whose “lay discourses” forge connections between the different bands
Harnessing generative AI for self-directed learning: Perspectives from top management
No full textPurposeThe purpose of this paper is to explore the potential of generative AI-driven self-directed learning from the perspective of top management in the Sri Lankan software industry. By applying open innovation theory, the study aims to understand how business leaders perceive the integration of generative AI tools in organizational learning processes. The insights gained are intended to inform and encourage top management to promote generative AI-driven self-directed learning within their organizations. Design/methodology/approach The research utilized a qualitative approach, conducting semi-structured interviews with eight senior managers from IT companies in Colombo, Sri Lanka. Data was synthesized and analyzed thematically to identify patterns and insights regarding generative AI-driven self-directed learning and its organizational impact. Findings The study reveals that top management in Sri Lanka's software industry perceives generative AI-driven self-directed learning positively. This perception is rooted in the alignment of such learning with open innovation principles, emphasizing knowledge sharing, collaboration and the integration of external expertise to drive innovation. Generative AI tools empower employees to access diverse knowledge sources, fostering continuous learning and adaptability. Leaders recognize these tools' potential to enhance organizational innovation ecosystems and competitive advantage. The findings suggest that active support from top management, customized training programs and a culture that embraces continuous learning and innovation are crucial for successful implementation. Originality/value This paper uniquely explores generative AI-driven self-directed learning through the lens of top management in Sri Lanka's software industry, integrating open innovation theory to highlight its potential in enhancing organizational knowledge, collaboration and competitive advantage
Staff-student co-creation in a matrix environment
No full textThe Solent Student Partnership Project is a cross-institutional co-creation scheme hosted by the Education Office at Solent University. This chapter discusses how and why, over a three-year period, the project has moved from being hierarchically managed to being operationalised through a matrix environment. The chapter starts by outlining the evolution of the co-creation initiative in our context. It then shares our mixed experiences of moving our project into a matrix environment via three themes of contradiction: (1) losing sight and gaining visibility, (2) cohesive and conflicting identities, and (3) ground moving and groundbreaking. The chapter concludes by reflecting on the lessons learned and proposes some possible ways forward for staff-student co-creation in a matrix environment
Towards transparent diabetes prediction: combining AutoML and Explainable AI for improved clinical insights
No full textDiabetes is a global health challenge that requires early detection for effective management. This study integrates Automated Machine Learning (AutoML) with Explainable Artificial Intelligence (XAI) to improve diabetes risk prediction and enhance model interpretability for healthcare professionals. Using the Pima Indian Diabetes dataset, we developed an ensemble model with 85.01% accuracy leveraging AutoGluon’s AutoML framework. To address the “black-box” nature of machine learning, we applied XAI techniques, including SHapley Additive exPlanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), Integrated Gradients (IG), Attention Mechanism (AM), and Counterfactual Analysis (CA), providing both global and patient-specific insights into critical risk factors such as glucose and BMI. These methods enable transparent and actionable predictions, supporting clinical decision-making. An interactive Streamlit application was developed to allow clinicians to explore feature importance and test hypothetical scenarios. Cross-validation confirmed the model’s robust performance across diverse datasets. This study demonstrates the integration of AutoML with XAI as a pathway to achieving accurate, interpretable models that foster transparency and trust while supporting actionable clinical decisions
Integrating sentiment analysis to enhance mental health support chatbots
No full textMental health conditions have adverse effects on an individual's quality of life. Between 2017 and 2019, there were 52.9 emergency department visits per 1,000 adults for mental health disorders. The rising popularity of artificial intelligence prompted its use in depression detection or mental condition diagnosis, establishing its prominent role in the mental health sector. In this research, we developed an AI chatbot with enhanced contextual intelligence using sentiment analysis to support individuals experiencing mental distress. For this research, we utilized a dataset of text data from 1.6 million posts on the social media platform X (formerly Twitter) due to its open-source availability and accessibility. The collected dataset was cleaned of repetitive characters, special characters, URLs, and numbers. NLP techniques, including tokenization, lemmatization, and stemming, were used for pre-processing. The application identifies sentiments, generates responses, and suggests basic support remedies. We used the Rasa framework to create a hybrid chatbot with customizable configurations. An LSTM model for sentiment analysis was integrated into the Rasa chatbot as a custom action component. A batch size of 32 and an optimal maximum sequence length were selected for balanced training efficiency and accuracy. The LSTM model achieved 76% accuracy in training and validation, enhancing the chatbot text comprehension. Future improvements will include adding personal features and expanding the user base
Data sources:
Access Repository Dashboard
Do you manage Solent University Research Portal? Access insider analytics, issue reports and manage access to outputs from your repository in the CORE Repository Dashboard!