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Coaches’ perspectives of the use of small-sided games in the professional soccer training environment
Get PDFObjective: The utilization and variation of small-sided games (SSGs) in team sports have garnered increased attention in recent years. This study aimed to explore the application of SSGs in high-performance soccer using qualitative methods. Methods: Five high-performance soccer coaches participated in semi-structured interviews. A reflexive thematic analysis was conducted, revealing six key themes: 1) the relevance of SSGs, 2) variations in SSGs, 3) the role of SSGs in planning and periodization, 4) the diverse functions and meanings of SSGs within a high-performance team, 5) decision-making and creativity in SSGs, and 6) the emphasis on tactical development through SSGs. Results: The findings highlighted the integral role of SSGs in the coaches’ training routines, particularly on microcycle days -4 and -3, and for both substitute and starting players. Coaches identified the number of players and pitch dimensions as primary constraints to manipulate. The leadership of SSGs was typically delegated to assistant coaches to enhance the physical and enjoyment aspects. SSGs were predominantly used to develop positional play, with specific playing positions constrained in various pitch areas to elicit targeted behaviors. Furthermore, SSGs were employed to enhance players’ decision-making and creativity by providing game-like scenarios that encourage spontaneous problem-solving. Conclusion: This study underscores the critical importance of SSGs in high-performance soccer training, offering practical insights for coaches and theoretical implications for researchers. Coaches can leverage SSGs to replicate match demands, foster tactical understanding, and enhance player engagement
Movies and the church of baseball: religion in the cinema of the national pastime
No full textChristian religious imagery and symbolism has a long history in American baseball cinema, from The Busher (1919) to Angels in the Outfield(1994) and present-day movies. This book examines The Natural, Field of Dreams, Bull Durham and other films, exploring the frequency of Christian imagery and themes in the American baseball movie. From Babe Ruth’s performance of a miracle to help a disabled boy walk again in The Babe Ruth Story to Shoeless Joe Jackson’s question to Ray Kinsella—”Is this heaven?”—in Field of Dreams, Christian themes and American baseball film are inextricably linked. This discussion encompasses symbolic imagery in mainstream film, Christian baseball movies directed by Christian filmmakers promoting their faith messages and images of America as a prelapsarian paradise before “The Fall.
Perceived discomfort is decreased after repeated bouts of isometric handgrip exercise with and without blood flow restriction
Get PDFBlood flow restricted exercise appears to be more discomforting than the same exercise without blood flow restriction. Changes in discomfort have not been investigated following repeated bouts of isometric exercise. It is possible that the isometric contractions may further trap metabolites resulting in greater discomfort. The purpose was to investigate the effects of six weeks of isometric handgrip exercise on perceived discomfort and willingness to continue with that form of exercise. 135 participants trained three times a week for six-weeks. The training consisted of four sets of 2-min low-intensity contractions (at 30% of their maximal voluntary contraction) with blood flow restriction (LI + BFR) and without blood flow restriction (LI). The maximal contraction group performed four, five second maximal contractions (MAX). Discomfort was measured post-exercise on the first, ninth, and last training session using the CR10+ scale. Changes in discomfort from the 1st to the 18th session were greater in the LI [-1.7 (1.7) AU] (BF10 = 6952.769) and LI + BFR [-1.5 (1.9) AU] (BF10 = 404.996) when compared to MAX group [0.04 (1.5) AU]. There was no difference between LI and LI + BFR (BF10 = 0.241). Although there were differences in discomfort, there was no difference in the desire to continue the same exercise amongst groups (BF10 = 0.208). Discomfort decreased more in both low intensity groups compared to the MAX group. Despite greater decreases in discomfort there was no difference in willingness to continue with the same form of exercise. This suggests other factors besides discomfort may influence an individual’s willingness to continue with the same type of exercise
CNN-based optimization for fish species classification: tackling environmental variability, class imbalance, and real-time constraints
Get PDFAutomated fish species classification is essential for marine biodiversity monitoring, fisheries management, and ecological research. However, challenges such as environmental variability, class imbalance, and computational demands hinder the development of robust classification models. This study investigates the effectiveness of convolutional neural network (CNN)-based models and hybrid approaches to address these challenges. Eight CNN architectures, including DenseNet121, MobileNetV2, and Xception, were compared alongside traditional classifiers like support vector machines (SVMs) and random forest. DenseNet121 achieved the highest accuracy (90.2%), leveraging its superior feature extraction and generalization capabilities, while MobileNetV2 balanced accuracy (83.57%) with computational efficiency, processing images in 0.07 s, making it ideal for real-time deployment. Advanced preprocessing techniques, such as data augmentation, turbidity simulation, and transfer learning, were employed to enhance dataset robustness and address class imbalance. Hybrid models combining CNNs with traditional classifiers achieved intermediate accuracy with improved interpretability. Optimization techniques, including pruning and quantization, reduced model size by 73.7%, enabling real-time deployment on resource-constrained devices. Grad-CAM visualizations further enhanced interpretability by identifying key image regions influencing predictions. This study highlights the potential of CNN-based models for scalable, interpretable fish species classification, offering actionable insights for sustainable fisheries management and biodiversity conservation
TwinGuard: privacy-preserving digital twins for adaptive email threat detection
No full textEmail continues to serve as a primary vector for cyber-attacks, with phishing, spoofing, and polymorphic malware evolving rapidly to evade traditional defences. Conventional email security systems, often reliant on static, signature-based detection struggle to identify zero-day exploits and protect user privacy in increasingly data-driven environments. This paper introduces TwinGuard, a privacy-preserving framework that leverages digital twin technology to enable adaptive, personalised email threat detection. TwinGuard constructs dynamic behavioural models tailored to individual email ecosystems, facilitating proactive threat simulation and anomaly detection without accessing raw message content. The system integrates a BERT–LSTM hybrid for semantic and temporal profiling, alongside federated learning, secure multi-party computation (SMPC), and differential privacy to enable collaborative intelligence while preserving confidentiality. Empirical evaluations were conducted using both synthetic AI-generated email datasets and real-world datasets sourced from Hugging Face and Kaggle. TwinGuard achieved 98% accuracy, 97% precision, and a false positive rate of 3%, outperforming conventional detection methods. The framework offers a scalable, regulation-compliant solution that balances security efficacy with strong privacy protection in modern email ecosystems
Rethinking critical race theory: education against elimination in a time of genocide
No full textThis open access book outlines a framework to explain how a world system of white supremacy is reproduced through education at national and local levels. This contribution thus addresses the important need for critical race education scholarship that is not limited by nationally-framed or American-centric perspectives. Written in an accessible manner, the framework recognises that racism takes hold in diverse ways in local and national educational contexts, but is ultimately rooted in global white supremacy. Forging links between Critical Race Theory and decolonial thought, this framework explains how national racialized social systems are reproduced through global structures, knowledge, and feelings; and articulates how anti-racism in education relies on forging transnational solidarities. To illustrate the relevance and significance of this framework, the book engages with how educational systems throughout the world are implicated in sustaining global anti-Muslim racism in a time of Palestinian genocide. Addressing a timely topic and gap in the literature, this book will provide an invaluable resource to postgraduate students and academics developing critical scholarship that explains and resists the global nature of race and racism in education
Antecedents of customer awareness in banking security: a protection motivation theory-based systematic review
No full textThis study examines the factors influencing customer awareness in personal banking security, with particular emphasis on the Sri Lankan context, where rising cyber-fraud incidents highlight the need for stronger customer awareness. While prior research has focused on technology adoption and security behavior, evidence on the antecedents of customer awareness remains fragmented. A PRISMA-guided systematic literature review of thirty-eight empirical studies indexed in Scopus was conducted using Protection Motivation Theory (PMT) as the primary analytical framework. The review synthesizes findings related to threat appraisal, coping appraisal, and additional factors influencing awareness in digital banking security. The results show that self-efficacy and response efficacy are the most consistent drivers of awareness, while perceived severity and vulnerability show mixed effects. Social influence, fear, and trust further shape awareness beyond PMT’s core constructs. The findings are contextualized to Sri Lanka, offering insights to strengthen local banking security awareness strategies
CNN-based optimization for fish species classification: tackling environmental variability, class imbalance, and real-time constraints
No full textAutomated fish species classification is essential for marine biodiversity monitoring, fisheries management, and ecological research. However, challenges such as environmental variability, class imbalance, and computational demands hinder the development of robust classification models. This study investigates the effectiveness of convolutional neural network (CNN)-based models and hybrid approaches to address these challenges. Eight CNN architectures, including DenseNet121, MobileNetV2, and Xception, were compared alongside traditional classifiers like support vector machines (SVMs) and random forest. DenseNet121 achieved the highest accuracy (90.2%), leveraging its superior feature extraction and generalization capabilities, while MobileNetV2 balanced accuracy (83.57%) with computational efficiency, processing images in 0.07 s, making it ideal for real-time deployment. Advanced preprocessing techniques, such as data augmentation, turbidity simulation, and transfer learning, were employed to enhance dataset robustness and address class imbalance. Hybrid models combining CNNs with traditional classifiers achieved intermediate accuracy with improved interpretability. Optimization techniques, including pruning and quantization, reduced model size by 73.7%, enabling real-time deployment on resource-constrained devices. Grad-CAM visualizations further enhanced interpretability by identifying key image regions influencing predictions. This study highlights the potential of CNN-based models for scalable, interpretable fish species classification, offering actionable insights for sustainable fisheries management and biodiversity conservation
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