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FIFA Women's World Cup 2023 Australia and New Zealand corner kicks: an analysis of key characteristics
No full textSet Pieces are a valuable method for performance analysts to derive tactical insights from football matches, with corners being particularly relevant due to their proximity to the goal and relative frequency. This study analyzed 601 corner kicks from 64 matches of the FIFA Women's World Cup 2023 to understand the key characteristics of effective corner kicks. Using notational analysis, footage from the Wyscout scouting platform was observed to collect data, which was then subjected to descriptive analysis. Reliability tests, including Kappa scores, confirmed the strength of agreement, validating the analysis tool and data collection process. The research found that 28 goals (4.7%) were scored following a corner kick sequence, with an additional 30 shots on target (excluding goals). These findings indicate a higher success rate compared to most studies on corner kicks in men's football and are like previous studies on woman's football. The most effective type of delivery was an out-swinging corner, with 7.1% resulting in a goal. In terms of defensive methods, mixed marking with individual dominance was the most effective, conceding goals after only 3.8% of corners. Teams effectively defended against offensive transitions after their only corner kicks, allowing only 2 shots and 1 goal from such situations. Overall, the study's results showed similarities to previous literature but also highlighted some differences between corner kicks in women's and men's football. These insights are valuable for football practitioners in developing and refining corner kick strategies
Responses to monetary policy uncertainty: an asymmetric analysis of money supply and demand
Get PDFThis study aims to examine the symmetric and asymmetric effects of uncertainties in US monetary policy on money demand between 2000: M1 and 2025: M5.To this end, this study explores and uses the recently developed monetary policy uncertainty (MPU) index, applying both linear and nonlinear autoregressive distributed lag (ARDL) models to the USA. The linear model could not find a significant effect of MPU on money demand (M2).The linear model could not find a significant effect of MPU on M2. However, empirical findings of the nonlinear ARDL indicate that uncertainties in monetary policy have essential impacts on M2. While rises in the MPU index decrease the demand for money, falls in the index increase in the long run. This can be interpreted as meaning that rising uncertainty in the MPU increases uncertainty about future interest rates, inflation and economic growth expectations, and individuals may become more cautious about spending or investing. However, when uncertainty decreases, Americans tend to increase their demand for money.Using the MPU index − rather than broader uncertainty indicators such as the EPU − provides a more focused perspective on monetary dynamics. Crucially, the findings offer forward-looking insights into how Americans may adjust their money-holding behavior in response to potential increases in MPU driven by evolving Federal Reserve policies under the current US administration’s 2025 high-tariff economic agenda. This enhances the study’s policy relevance for anticipating behavioral responses to future uncertainty
Corporate governance and sustainability disclosure: challenges and opportunities for the Libyan Audit Bureau in the oil sector
Get PDFThis study identifies challenges faced by Libyan Audit Bureau (LAB) members in monitoring governance practices and sustainability disclosure in Libya's oil sector. Using quantitative data from 231 distributed questionnaires (88% response rate), the research reveals that LAB oversight remains in early stages due to persistent challenges including regulatory gaps, weak enforcement, insufficient professional standards, and cultural‐institutional barriers. While international benchmarks such as IFRS S1 and S2 gain worldwide adoption, Libya's implementation remains limited. Enhanced LAB effectiveness requires policies addressing stakeholder rights, Islamic principles integration, transparency mechanisms, conflict management, and external factors including professional development, legal system strengthening, enforcement improvement, cultural considerations, and awareness building for governance and sustainability reporting
Creation of a novel coding program to identify genes controlled by miRNAs during human rhinovirus infection
Get PDFHuman rhinovirus (RV) is the most frequent cause of the common cold, as well as severe exacerbations of chronic obstructive pulmonary disease (COPD) and asthma. Currently, there are no effective and accurate diagnostic tools or antiviral therapies. MicroRNAs (miRNAs) are small, non-coding sections of RNA involved in the regulation of gene expression and have been shown to be associated with different pathologies. However, the precise role of miRNAs in RV infection is not yet well established. Also, no unified computational framework exists to specifically link miRNA expression with functional gene targets during RV infection. This study aimed to first analyse the impact of RV16 on miRNA expression across the viral life cycle to identify a small panel with altered expression. We then developed a novel bioinformatics pipeline that integrated time-resolved miRNA profiling with multi-database gene-phenotype mapping to identify diagnostic biomarkers and their regulatory networks. Our in-house Python-based tool, combining mirDIP, miRDB and VarElect APIs, predicted seven genes (EZH2, RARG, PTPN13, OLFML3, STAG2, SMARCA2 and CD40LG) implicated in antiviral responses and specifically targeted by RV16 and regulated by our miRNAs. This method therefore offers a scalable approach to interrogate miRNA-gene interactions for viral infections, with potential applications in rapid diagnostics and therapeutic target discovery
Estimating the replicability of Sports and Exercise Science research
No full textBackgroundThe replicability of sports and exercise research has not been assessed previously despite concerns about scientific practices within the field.AimThis study aims to provide an initial estimate of the replicability of applied sports and exercise science research published in quartile 1 journals (SCImago journal ranking for 2019 in the Sports Science subject category; www.scimagojr.com) between 2016 and 2021.MethodsA formalised selection protocol for this replication project was previously published. Voluntary collaborators were recruited, and studies were allocated in a stratified and randomised manner on the basis of equipment and expertise. Original authors were contacted to provide deidentified raw data, to review preregistrations and to provide methodological clarifications. A multiple inferential strategy was employed to analyse the replication data. The same analysis (i.e. F test or t test) was used to determine whether the replication effect size was statistically significant and in the same direction as the original effect size. Z-tests were used to determine whether the original and replication effect size estimates were compatible or significantly different in magnitude.ResultsIn total, 25 replication studies were included for analysis. Of the 25, 10 replications used paired t tests, 1 used an independent t test and 14 used an analysis of variance (ANOVA) for the statistical analyses. In all, 7 (28%) studies demonstrated robust replicability, meeting all three validation criteria: achieving statistical significance (p < 0.05) in the same direction as the original study and showing compatible effect size magnitudes as per the Z test (p > 0.05).ConclusionThere was a substantial decrease in the published effect size estimate magnitudes when replicated; therefore, sports and exercise science researchers should consider effect size uncertainty when conducting subsequent power analyses. Additionally, there were many barriers to conducting the replication studies, e.g., original author communication and poor data and reporting transparency
The validity and reliability of the my jump lab artificial intelligence application
No full textJump height (JH) achieved in a countermovement jump (CMJ) has been suggested to allow for the monitoring of neuromuscular fatigue (NMF) and assessment of lower body power. Although force platforms (FP) are considered the gold standard for measuring CMJ height, they are expensive compared to mobile apps such as My Jump Lab (MJL). Therefore, this study aimed to assess the concurrent validity and agreement of the MJL app compared to a FP (ForceDecks [FD]) system and to determine its test-rest reliability. A convenience sample of 26 (n = 11 females and n = 15 males) recreationally active university sport students and staff (mean ± SD; age: 23.08 ± 6.33 years; mass: 72.85 ± 9.93 kg; stature: 176.63 ± 10.18 cm) participated in the study. Participants attended the laboratory for testing on two separate occasions, separated by one week. After a standardised warm-up, they completed three CMJs on each occasion, with CMJ height simultaneously assessed by the FD and MJL app. The MJL Artificial Intelligence mode showed a mean bias of 4.32 cm [95% CI: 3.4, 5.26] overestimation with 95% limits of agreement ranging from -3.33 cm [95% CI: -4.96, -0.85] to 11.98 cm [95% CI: 10.13, 13.41]. Both methods demonstrated minimal mean bias (FD = 0.61 cm [95% CI: -0.31, 1.37] and MJL = 0.25 cm [95% CI = -0.48, 0.98]) between sessions, and both showed a similar width to their limits of agreement, ranging ~7 cm about the mean bias. In summary, the MLJ overestimated CMJ height in this sample compared to the FD system, but both methods were reliable. Given the significant differences in cost for these two methods, teams on a budget may interested in trialling the MJL app.
A structural causal model ontology approach for knowledge discovery in educational admission databases
No full textEducational 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
Semantic similarity in community forum questions: case study on Quora dataset
No full textDuplicate questions on crowd-sourced question and answer websites such as Quora create redundancy and make information retrieval inefficient. This research conducts a systematic comparative analysis of machine learning and deep learning models for detecting semantic similarity in questions. Using the Quora Question Pairs dataset, we evaluate a spectrum of models: a classical TF-IDF baseline, feature-engineered Random Forest and XGBoost, a Siamese Manhattan LSTM (MaLSTM), and a fine-tuned BERT model. The study reveals a clear performance hierarchy. A key finding is that classical models with a limited set of hand-crafted linguistic features underperformed the simple TF-IDF baseline. While the MaLSTM network showed moderate improvement, the fine-tuned BERT model was unequivocally superior, achieving a statistically significant accuracy of 86.26%. This highlights the critical role of deep contextual embeddings for this task. However, BERT’s state-of-the-art performance comes at a significant computational cost, revealing a crucial trade-off between accuracy and resource efficiency. These findings provide a pragmatic guide for designing effective and scalable duplicate question detection systems
A lab-on-a-chip system integrating DNA purification and loop-mediated isothermal amplification for the quantification of the toxic diatom <i>Pseudo-nitzschia multistriata</i>
No full textMicrofluidic technology can expedite nucleic acid testing by converting the functions of bulky laboratory instruments and protracted bench methodologies into easy-to-use and inexpensive miniaturised systems without compromising speed and reliability. We developed a lab-on-a-chip (LOC) platform that integrates a dimethyl adipimidate (DMA)-based functionalised silica DNA isolation and pre-concentration method with a rapid and real-time loop-mediated isothermal amplification (LAMP) for detecting domoic acid-producing phytoplankton, Pseudo-nitzschia. An optimised design of a lab on a chip extraction module achieved a maximum DNA capture capacity of 61.73 ± 0.98 ng μL−1. The DMA-based method reduced reagent costs per sample by 97% compared to a commercial nucleic acid isolation kit. A subsequent on-chip LAMP process was capable of sensitively quantifying cytochrome P450 homologous to the dabD gene, coding for a component of the domoic acid toxin production pathway, with a limit-of-detection of 10 cells per mL. LAMP-based detection of the target gene was achieved using dry-preserved reagents with a shelf-life of five months without refrigeration. There was no significant difference in assay performance between the preserved LAMP and freshly prepared LAMP mixtures. The total analysis time at the LOD of 10 cells per mL, from sample to result, was achieved within one hour. Our results demonstrate the long-term stability of assay reagents, rapid turnaround, and cost-effectiveness, offering a simple and economical approach to environmental monitoring and environmental bio-hazard diagnostics
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