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Relative age effects in European soccer: their association with contextual factors, impact on youth national teams' performance, and presence at the senior level
No full textIntroduction: Soccer systems promote early identification and specialisation practices to satisfy short- and long-term goals—both from sporting performance and financial gains perspectives. In this context, players are (de)selected based on observed performance level and on their ability to conform to given organisational demands, leading to the proliferation of selection biases, such as relative age effects (RAEs), which research has shown to influence both developmental experiences and senior career achievements. Accordingly, this study aims to: (a) investigate the magnitude of RAEs among youth national teams competing in the UEFA U17 European Soccer Championship, and their associations with teams' final ranking, (b) examine whether RAEs magnitude could be linked to cultural and contextual factors, and (c) further explore RAEs at senior level.
Methods: Birth quarter (BQ) distribution of youth national teams (n = 80) that competed in one of the five editions (2018, 2019, 2022, 2023, and 2024) of the UEFA U17 European Soccer Championship was recorded. Teams were classified based on their country of origin, RAEs magnitudes, final ranking in the tournament, FIFA points, and national population. Furthermore, the BQ distribution of senior national teams (n = 24) that competed at the 2024 UEFA Senior European Soccer Championship was recorded.
Results: Chi-square statistics revealed BQ1s were overrepresented at the U17 level (p < 0.001) and showed teams exhibiting low RAEs magnitudes recorded the highest likelihood (odds ratio: 5.67) of finishing the tournament in the bottom four positions. Correlation analyses recorded small to moderate positive correlations between RAEs magnitude and national population (.25) and FIFA points (.33). Further chi-square statistics revealed BQ1s continued to be overrepresented at the senior level, albeit with a weaker effect (p < 0.001). However, when the senior BQ distribution was compared to the expected distribution taken from the U17 population, this recorded more BQ4s and fewer BQ1s than expected (p < 0.001).
Discussion: The findings presented the focus on youth success, the increased talent pool size, and the competition for selection interact to reiterate RAEs' prevalence in European soccer. Moreover, they highlighted initial RAEs define players' journey within the soccer system, whereby relatively older players remain overrepresented at the senior level, albeit to a weaker and lesser extent
Key Stakeholders’ Perspectives on the Sports Science and Medicine Resources and Practices in English Non-League Male Football
No full textBackground: Sports science and medicine (SSM) is integral to professional football clubs. The level below professional football in England, ‘non-league football’ (NLF), consists of full-time and part-time clubs. The existing literature has exclusively focused on SSM in professional football, with the resources and practices in NLF currently unknown. Therefore, this study explored the SSM resources and practices within NLF by investigating the perspectives of key stakeholders working within NLF coaching and SSM disciplines. Methods: Fifty participants (coaching practitioners [n = 25] and SSM practitioners [n = 25]) from NLF clubs completed an anonymous online survey comprising 31 multiple-choice and Likert-scale questions, alongside optional open-ended comments. Results: Support was mixed for SSM evidence-based practices across clubs in Tiers 5–10 within the National League System. The most common SSM resources were the training ground (n = 39), resistance training equipment (n = 15), and rehabilitation area (n = 13). Fitness testing was frequent (86%) pre-season but rare end-of-season (8%). Workload monitoring primarily consisted of the session duration (80%) and time–motion data (36%). Performance analysis of competitive matches commonly used video (74%) or post-match technical analysis (40%). Injury monitoring generally occurred ‘always’ (44%) or ‘sometimes’ (28%). Nutritional support on match days was mostly fluids (80%), with ‘no support’ reported most outside match days (54%). Conclusions: The SSM resources and practices vary considerably within NLF, influenced by individual club constraints and barriers, including financial support, access to facilities, and equipment availability. These findings may inform future SSM provisions in NLF to enhance team performances and player availability
Enhanced Anomaly Detection in Wireless 5G Networks With Hybrid Learning Technique Using AWID3 Dataset
No full textIn recent years, the expansion of the Internet of Things and 5G networks has significantly increased wireless traffic, heightening the risk of cyberattacks. Intrusion detection systems have become essential for safeguarding wireless networks by providing real-time threat detection and response. This study presents a comprehensive review and implementation of machine learning-based techniques for detecting various types of wireless attacks, with a focus on improving detection accuracy through ensemble learning. The AWID3 dataset, based on the IEEE 802.11 standard, was used for experimentation. The study was conducted in multiple phases: (1) evaluating six machine learning algorithms (random forest, J48, naïve Bayes, logistic regression, decision tree, and deep neural networks) using three feature selection methods (information gain, gain ratio, and chi-squared); (2) developing a hybrid ensemble model by integrating the strengths of deep neural network, random forest, XGBoost, and LightGBM, with logistic regression as a meta-classifier; and (3) validating performance using key metrics: accuracy, precision, recall, and F1-score. The proposed hybrid model achieved a peak accuracy of 99.75%, outperforming benchmark models in the literature. These results demonstrate the superior performance and robustness of the proposed hybrid approach. By addressing multiple network layers and leveraging ensemble learning, this research highlights the critical role of hybrid models in achieving reliable and accurate intrusion detection for wireless environments
Design and Development of a Small-Scale Green Hydrogen Vehicle: Hydrogen Consumption Analysis under Varying Loads for Zero-Emission Transport
No full textWith growing interest in its potential applications across both stationary and transportation sectors, hydrogen has emerged as a promising alternative for environmentally responsible power generation. By replacing traditional fuels, hydrogen can significantly reduce greenhouse gas emissions in the transportation sector. This study focuses on the design and downsizing of a green hydrogen fuel cell car, aiming to scale the concept for larger vehicles. Key components, including fuel cells, electrolysers, and solar panels, were evaluated through extensive laboratory testing. The findings reveal that variations in sunlight impact the solar panel’s hydrogen production rate, with differences of approximately 4.9% attributed to changes in time and date. Analysis of consumption rates showed that a 17.4% increase in current consumption leads to a significant reduction in operational time. Further testing under varying loads demonstrated that higher current demands, such as those from a DC motor, accelerate hydrogen depletion, whereas lower currents extend operational duration. These results underscore the importance of maximizing solar energy efficiency, reducing reliance on conventional energy sources, and regulating consumption rates to optimize fuel cell performance. Since hydrogen is produced using renewable energy, fuel cell technology is virtually emission-free. Additionally, the study highlights the viability of powering vehicles with renewable energy, emphasizing the potential of green hydrogen fuel cell technology as a sustainable transportation solution
Integrating Ecological, Productive, and Macrofinancial Spheres with ESTEEM: A System Dynamics Framework to Assess Brazil’s Transformation Plan
No full textThis paper presents a continuous-time behavioural ecological macroeconomic model grounded in the dynamic input–output (IO) framework, named ESTEEM, and applies it to the Brazilian economy. The model is calibrated using Brazil’s IO matrix, and its primary goal is to serve as a policy and scenario-building toolbox, illustrated here through the Brazilian Economic Transformation Plan (Plano de Transformação Ecológica), announced at COP28 in 2023. Tailored for open developing economies, the model extends traditional IO analysis by integrating dynamic feedback loops, sectoral investment behaviour, inventory dynamics, wage and price formation, environmental pressures and constraints, and a range of policy instruments. Combining structuralist foundations with system dynamics, ESTEEM captures both short-term disequilibrium and long-term development paths, allowing simulations of industrial policy, fiscal and monetary interventions, structural change, and ecological transitions. Key innovations include the endogenisation of capital accumulation, adaptive expectations, and green technological change
Inclusive Immersive Technology in Industry 5.0: Considering Spatial Computing Barriers for Users With Physical Impairments
No full textIndustry 5.0 offers the potential to reshape manufacturing processes and aims to improve the working environment for all users. The promise of the natural integration of immersive technologies, namely, augmented reality and virtual reality (AR/VR) and usable spatial interfaces, with traditional industry processes presents an opportunity to improve efficiency, accuracy, training, and collaboration. However, for Industry 5.0 to deliver on this potential, it is paramount that fully inclusive systems are created by placing all users at the center of the AR/VR spatial interface design, development, and implementation. This article discusses hurdles that could manifest in Industry 5.0 AR/VR spatial interfaces for users with physical impairments. We discuss the challenges that have been reported in prior academic literature specifically relating to software, hardware, ethics, and collaboration and connect these to spatial interface elements for potential Industry 5.0 uses of AR/VR technology. We present six indicative Industry 5.0 spatial interface scenarios, which cover a spectrum of potential applications ranging from training though to collaboration, and illustrate where these barriers may manifest for users with a physical impairment. While we do not present an exhaustive list of scenarios, we present a representation of tasks and a starting point for discussion, which can inform developers, designers, and researchers on how to consider a more inclusive approach to spatial Industry 5.0 interfaces
Few-Shot Learning With Prototypical Networks for Improved Memory Forensics
No full textSecuring computer systems requires effective methods for malware detection. Memory forensics analyzes memory dumps to identify malicious activity, but faces challenges including large and complex datasets, constantly evolving malware threats, and limited labeled data for training algorithms among others. This research introduces a novel approach for malware detection using memory forensics and prototypical networks. As the first application of prototypical networks to the Dumpware10 dataset (to the best of authors knowledge), our findings highlight the potential of few-shot learning for memory forensics-based malware detection, opening new avenues for research in this domain. Prototypical networks are a type of few-shot learning algorithm that excels at classifying new categories with minimal examples. Utilizing the publicly available Dumpware10 dataset, which includes 10 malware classes and one benign class, we preprocess memory dumps using denoising and A-Hash functions to reduce noise and redundancy. The prototypical network is trained on the first four malware classes and the benign class. It’s then tested on a dataset with one additional class (first five malware classes and the benign class). We progressively increase the number of test classes to eleven. Within each training episode, five training images are used as support samples, with all remaining images designated as query samples. Our goal isn’t to predict exact class labels, but to assess the similarity between query images and prototypes using a distance metric. If the label of a prototype matches the query image and the distance falls below a threshold, it’s considered a true positive. This approach achieves an average accuracy of 92% with eleven classes, the highest across all scenarios and comparable to previous work using machine and deep learning algorithms on this dataset
From shadow to sustainability: How informality, environmental taxes, and green innovation reshape carbon and biodiversity futures in the G7 countries
No full textThe shadow economy remains a blind spot in climate-and-biodiversity policy. However, its interaction with fiscal and technological forces can significantly affect the success or failure of sustainability transitions. We propose a novel integrated framework that combines econometric models with deep learning to examine the role of the shadow economy, environmental taxes and green innovation on consumption-based CO2 emissions and biodiversity in the G7 countries. Using data from 1994 to 2020, the study employs Cross-sectionally Autoregressive Distributed-lag (CS-ARDL) and Fully Modified Ordinary Least Squares (FMOLS) to estimate the relationship among the variables. Moreover, deep learning models—Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN)—are applied to quantify and forecast the relationship between these factors. The study finds that the shadow economy increases environmental degradation. Whilst, green innovation and environmental taxes improve both emissions reduction and biodiversity productivity. Forecasts to 2030 indicate that without reducing the shadow economy, effective tax enforcement and green innovation, the G7 will likely to miss decarbonization and persistent biodiversity loss. The findings highlight the need for integrated policies for reducing the shadow economy with effective environmental taxes and sustainability-focused innovation
Caffeine supplementation for soccer: A review of strengths, limitations, and knowledge gaps
No full textCaffeine is a well-established ergogenic aid with a wealth of evidence demonstrating beneficial effects for physical performance, cognitive function and sport specific skills. Intuitively, it may be considered that such effects may translate to improved soccer specific performance, however, evidence examining the effects of caffeine on the interacting demands of soccer match play is sparse. Given that caffeine supplementation is highly prevalent in professional soccer, and in a number of cases practices adopted lack supporting evidence, this review evaluates the current state of the knowledge regarding the ergogenic potential of acute caffeine consumption specifically for soccer performance. Furthermore, this review identifies knowledge gaps to guide future research, and whilst considering the unique environmental constraints, uses the available evidence to develop practical guidelines for safe and effective use