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Chaos Caused by Different Cutoff Dates: Relative Age Effects and Redshirting in Collegiate Volleyball in the United States
No full textRelative Age Effects (RAEs) are a phenomenon in athletics related to an over-representation of individuals born closer to an arbitrary cutoff date. Such effects have been shown in many different countries, levels of play, and contexts, although they are yet to be studied in volleyball within the United States, which is the second most popular high school girls’ sport and the fastest growing high school and college sport for males. Therefore, the purpose of this study was to examine RAEs in college volleyball. Publicly available data were collected from the websites of women’s Division I program (n = 1253) and men’s Division I/II (n = 164). Chi-squared goodness of fit tests were used to compare birth rate distributions. Data accounted for gender, school and club cutoff dates, athletic timing, and redshirt status. Results showed RAEs were strongest in women on-time school group. Interestingly, reverse effects were observed (i.e., an overrepresentation of relatively younger athletes) for delayed school volleyball players, but this expected trend was not observed in the redshirt group. On-time women’s club group showed academic timing was a significant contributor towards RAEs, whilst these effects were strongest for the on-time school group in men
Linking Organizational Dynamics to Digital Capabilities and ESG Outcomes: A Market-Oriented Approach
No full textEnvironmental, Social, and Governance (ESG) represent a concept of sustainable development that integrates corporate governance, social responsibility, and environmental concerns. Most ESG research focuses on large-size publicly traded companies, and limited attention is paid to entrepreneurial firms. Our study explores how market orientation, organizational leadership, and organizational culture lead to digital capabilities and ESG performance of Chinese entrepreneurial organizations. This study outlines a comprehensive framework grounded on market orientation and resource-based theories. Using structural equation modeling, we empirically test our hypotheses where findings indicate that organizational leadership, culture, and market orientation positively influence both digital capabilities and ESG performance. Additionally, we observed that cultural factors insignificantly affect digital capabilities. This research contributes to understanding corporate sustainability for academics, stakeholders, regulators, and policymakers, offering key theoretical and practical insights for advancing corporate sustainability
Vascular, inflammatory and perceptual responses to hot water immersion: Impacts of water depth and temperature in young healthy adults
No full textRepeated hot water immersion can improve cardiovascular health; however, the respective effects of distinct immersion protocols remain unclear. Twenty-two healthy adults completed three 30-min hot water immersion bouts of different water temperatures and immersion depths (40°C shoulder-deep immersion, 40-Shoulder; 42°C waist-deep immersion, 42-Waist; and 40°C waist-deep immersion, 40-Waist) in a randomised crossover design. Vascular, inflammatory and perceptual responses were collected via brachial and superficial femoral artery ultrasound, venous blood sampling and perceptual scales. Rectal temperature increased less in the 40-Waist (Δ0.5 ± 0.1°C) condition than the other conditions (40-Shoulder: Δ0.9 ± 0.3°C, 42-Waist: Δ0.9 ± 0.3°C, P < 0.001). Arm skin temperature increased more in the 40-Shoulder (Δ5.2 ± 1.9°C) condition than the other conditions (40-Waist: Δ2.6 ± 1.0°C, 42-Waist: Δ3.6 ± 1.1°C, P < 0.001), whilst thigh temperature had a greater increase in the 42-Waist (8.6 ± 1.3°C) condition than either the 40-Waist (7.8 ± 0.2°C) or 40-Shoulder (Δ7.8 ± 1.0°C) conditions (P < 0.001). Brachial artery shear rate was greatest post-immersion following the 40-Shoulder condition (40-Shoulder: Δ121 ± 94/s, 42-Waist: Δ47 ± 73/s, 40-Waist: Δ−21 ± 41/s, P < 0.001) whereas superficial femoral artery shear rate was largest following the 42-Waist condition (40-Shoulder: Δ143 ± 61/s, 42-Waist: 196 ± 85/s, 40-Waist: 131 ± 93/s, P < 0.001). IL-6 (P = 0.16) and cortisol (P = 0.83) responses did not differ between conditions. Perceptual responses were more favourable in the 40-Waist condition. Taken together, these data demonstrate that the distinct region-specific arterial responses align with increases in local skin temperature to alterations in hot water immersion protocols, whilst showing that beneficial physiological responses may be accompanied with less favourable perceptual responses
Gender differences in tweets on postnatal depression: A corpus linguistic analysis
No full textGender differences have been found in the way parents communicate online, however it is unclear whether these differences apply in the context of postnatal depression (PND). This research aimed to evaluate online discourses surrounding PND and explore gender differences in communication style associated with PND. X (formerly Twitter) data (15,850 posts) was identified and collected based on a key term search (e.g. ‘PND’) and analysed using corpus linguistic analysis. Results showed that female X users were more likely to discuss PND using words with a negative connotation or to use self-referent items, compared to male users who discussed PND more generally. X content related to PND was mostly created by female users and generally revolved around the experiences of mothers. The limited discussion regarding paternal PND suggests a lack of acknowledgement and insufficient online resources available for fathers
Review of Advances in Renewable Energy-Based Microgrid Systems: Control Strategies, Emerging Trends, and Future Possibilities
No full textThis paper gives a thorough overview of the technological advancements in microgrid systems, focusing on the Internet of Things (IoT), predictive analytics, real-time monitoring, architectures, control strategies, benefits, and drawbacks. It highlights their importance in boosting system security, guaranteeing real-time control, and increasing energy efficiency. Accordingly, researchers have embraced the involvement of many control capacities through voltage and frequency stability, optimal power sharing, and system optimization in response to the progressively complex and expanding power systems in recent years. Advanced control techniques have garnered significant interest among these management strategies because of their high accuracy and efficiency, flexibility and adaptability, scalability, and real-time predictive skills to manage non-linear systems. This study provides insight into various facets of microgrids (MGs), literature review, and research gaps, particularly concerning their control layers. Additionally, the study discusses new developments like Supervisory Control and Data Acquisition (SCADA), blockchain-based cybersecurity, smart monitoring systems, and AI-driven control for MGs optimization. The study concludes with recommendations for future research, emphasizing the necessity of stronger control systems, cutting-edge storage systems, and improved cybersecurity to guarantee that MGs continue to be essential to the shift to a decentralized, low-carbon energy future
Tool wear analysis of turning Ti-6Al-4V under dry, wet and cryogenic conditions
No full textEconomy of a manufacturing system is symbolic of its wholesome efficiency. Machining of difficult-to-cut titanium alloys has persistently presented a challenge due to its high temperature strength, low thermal conductivity and poor modulus of elasticity. In addition to the loss of economy owing to accelerated tool wear, the machine downtime also degrades manufacturing system efficiency. In the present research, effects of machining parameters and cooling systems were analyzed in terms of tool wear, which is a key productivity index, during turning of Ti-6Al-4V. Machining environments including wet and cryogenic conditions as well as dry machining was employed for experimentation. It was seen that tool wear can significantly be reduced by use of correct machining parameters in combination with appropriate cooling system. Cryogenic condition was found to cause 23% and 12% lesser wear than corresponding dry and wet machining at 60 m/min. SEM imagery identified adhesion and diffusion dissolution as the main wear inducing phenomena. ANOVA results identified cutting speed as the most substantial factor affecting tool wear with contribution ratio of 52.96%. Tool chip contact analysis depicted the lesser contact area under the usage of coolant reducing tool wear
Adaptive Intrusion Detection System with Ensemble Classifiers for Handling Imbalanced Datasets and Dynamic Network Traffic
No full textIntrusion Detection Systems (IDS) are crucial for network security, but their effectiveness often diminishes in dynamic environments due to outdated models and imbalanced datasets. This paper presents a novel Adaptive Intrusion Detection System (AIDS) that addresses these challenges by incorporating ensemble classifiers and dynamic retraining. The AIDS model integrates K-Nearest Neighbors (KNN), Fuzzy c-means clustering, and weight mapping to improve detection accuracy and adaptability to evolving network traffic. The system dynamically updates its reference model based on the severity of changes in network traffic, enabling more accurate and timely detection of cyber threats. To mitigate the effects of imbalanced datasets, ensemble classifiers, including Decision Tree (DT) and Random Forest (RF), are employed, resulting in significant performance improvements. Experimental results show that the proposed model achieves an overall accuracy of 97.7% and a false alarm rate (FAR) of 2.0%, outperforming traditional IDS models. Additionally, the study explores the impact of various retraining thresholds and demonstrates the model's robustness in handling both common and rare attack types. A comparative analysis with existing IDS models highlights the advantages of the AIDS model, particularly in dynamic and imbalanced network environments. The findings suggest that the AIDS model offers a promising solution for real-time IDS applications, with potential for further enhancements in scalability and computational efficiency
Measuring Carbon Emission Efficiency in a Developing Country: A Comparative Study of Sustainability Initiatives and Nonsustainability Initiatives of Manufacturing Firms
No full textImproving carbon emission efficiency (CEE) is considered one of the most cost‐effective ways to enhance sustainability and address climate change mitigation in developing countries like India. This study analyzes manufacturing firms' CEE of sustainability initiatives (SI) and nonsustainability initiatives (non‐SI) and examines the effect of nationally determined contributions commitments on these firms. A nonradial slack‐based model is adopted to assess efficiency over the period 2012–2022. Furthermore, the Tobit model is used to evaluate the influencing factors that promote the CEE of manufacturing firms. Findings reveal that most manufacturing firms in India, both in the SI and non‐SI groups, are carbon inefficient due to pure carbon emission inefficiency. Results also indicate that workforce skills training (0.23%) and technological progress (0.14%) positively impact the CEE of manufacturing firms. In addition, SI firms support the Porter hypothesis, suggesting that strict emissions regulations improve efficiency and encourage innovation. Therefore, policymakers in developing countries should implement performance‐based policies through trading programs and focus on skilled labor training and technological advancements to address climate change mitigation better and promote sustainability
NHS Health Check Focus Groups Report
No full textTo better understand this issue, the study engaged a total of 193 participants, including 13 NHS Health Check professionals, through focus groups representing a wide range of ethnic backgrounds. These sessions provided rich qualitative data on both user experiences and the perspectives of healthcare providers. Participants represented 10 distinct global majority groups: Arabs, Bangladeshi, Black Caribbean, Chinese, Ghanaian, Indian, Nigerian, Pakistani, Somali, and White British. The average age of all participants was 52.1 years. Of the total participants, 153 were within the NHS Health Check eligible age range (40-74 years). Total 45 participants reported having had an NHS Health Check. This figure includes only members of the public who were eligible for and attended the checks and does not include NHS Health Check staff who participated in the study. Only 5 participants had some form of disability
Predictive Modelling of Incident Risk to Pre-empt Risk in Highway Operations: A Machine Learning Approach
No full textHighway traffic officers (HTOs) operate in complex and hazardous environments, yet transportation safety research has predominantly focused on drivers, pedestrians, roads, and vehicles, with limited attention to HTOs' safety. In the UK, National Highways currently employs traditional statistical methods to mitigate safety risks post-incident, a reactive approach that does little for risk prevention. This thesis proposes a proactive approach by developing a machine learning (ML) prediction model to forecast incidents such as injuries, incursions and environmental hazards, assess risk levels, and predict the body parts likely to be affected in injurious events. The aim is to provide highway safety authorities with predictive insights for timely interventions and enhanced risk management. Despite the growing application of ML in safety risk prediction, there is limited evidence on the reliability of variables used as indicators of safety performance. To address this gap, this study develops a conceptual framework for selecting optimal safety indicators (SIs) and formulating input variables that enhance ML-based risk prediction. A three-stage, multiphase mixed-methods research design was employed: i) developing the conceptual framework; ii) constructing the proof-of-concept ML model; and iii) validating the model’s performance. The conceptual framework was established through a systematic literature review using PRISMA-based bibliometric search, scientometric and cluster analysis to identify significant SIs and grounded theory analysis was used for synthesis. The ML model development phase applied supervised learning algorithms, including Support Vector Machine (SVM), Random Forest (RF), Naïve Bayes (NB), Deep Neural Networks (DNN), Ensemble Learning (EL), and Recurrent Neural Networks (RNN). The models were trained using secondary data from a highway incident database and three data balancing techniques were tested to address the class imbalance. Model validation employed a stratified k-fold cross-validation approach, evaluated based on AUROC, precision, recall, and accuracy.
The study identifies key considerations for selecting SIs, emphasizing the integration of leading and lagging indicators to enhance system adaptability and resilience. A novel conceptual framework is presented that guides the selection of robust indicators for ML-based risk modelling. Empirical findings indicate that the SVM model with a polynomial kernel, combined with the SMOTE algorithm, outperforms other models in predicting incident types, risk levels, and affected body parts, whereas Random Under-sampling (RU) was the least effective. Critical factors influencing highway incidents, including weather conditions, visibility, age range and location, were identified and analysed.
This research makes several novel contributions: i) a novel conceptual framework integrating resilient SIs for predictive modelling; ii) a systematic approach to combining leading and lagging indicators for enhanced safety performance; and iii) the first study to use an incident database dedicated to HTOs for predictive risk modelling. The developed ML model provides actionable insights for safety officers, enabling proactive risk mitigation through targeted training and preparedness strategies for HTOs. This ultimately improves workplace safety in highway operations