Early Leak and Burst Detection in Water Pipeline Networks Using Machine Learning Approaches

Leakages in water distribution networks pose a formidable challenge, often leading to substantial water wastage and escalating operational costs. Traditional methods for leak detection often fall short, particularly when dealing with complex or subtle data patterns. To address this, a comprehensive comparison of fourteen machine learning algorithms was conducted, with evaluation based on key performance metrics such as multi-class classification metrics, micro and macro averages, accuracy, precision, recall, and F1-score. The data, collected from an experimental site under leak, major leak, and no-leak scenarios, was used to perform multi-class classification. The results highlight the superiority of models such as Random Forest, K-Nearest Neighbours, and Decision Tree in detecting leaks with high accuracy and robustness. Multiple models effectively captured the nuances in the data and accurately predicted the presence of a leak, burst, or no leak, thus automating leak detection and contributing to water conservation efforts. This research demonstrates the practical benefits of applying machine learning models in water distribution systems, offering scalable solutions for real-time leak detection. Furthermore, it emphasises the role of machine learning in modernising infrastructure management, reducing water losses, and promoting the sustainability of water resources, while laying the groundwork for future advancements in predictive maintenance and resilience of water infrastructure

Achieving success in post-graduate studies: A qualitative exploration of staff experiences transitioning from traditional to block mode delivery in an Australian university

Responding to student demand for flexibility in the delivery of classes as well as the potential barriers and enabling factors supporting student success, universities have introduced distinctive educational models, including replacing the standard 12-week, sequential delivery of units of study with 4- or 8-week blocks of one or two units at a time (Block Model). Most Block Model delivery and evaluation has been at the undergraduate level. The aim of this study was to investigate, via individual interviews, university staff experiences of teaching the Block Model at the postgraduate level. Staff affirmed that the Block Model resulted in high levels of engagement in teaching and learning; effective learning management systems; and up-to-date, engaging, and problem-based module design. However, staff also recognised the timetabling of units for students who were employed or had family commitments, and the cognitive burden carried by time-poor staff

Transformational Leadership, Psychological Safety, and Concussion Reporting Intentions in Team-Sport Athletes

Background/Objectives: The present study examined the predictive relationships between transformational leadership, psychological safety, and concussion reporting intentions. Interviews were used to understand the impact of the coach and teammates on the team environment and how this might lead to greater concussion reporting in team-sport athletes. Methods: This study employed a mixed-methods explanatory sequential design. 233 team-sport athletes (n = 160 males, n = 73 females, mean age = 19.83 years, SD = 3.15) completed quantitative measures of transformational leadership, psychological safety, and concussion reporting intentions, while five participants (n = 2 males, n = 3 females, mean age = 18.40 years, SD = 0.55) were subsequently interviewed about their experiences. Results: Quantitative results indicated that transformational leadership predicted a psychologically safe environment (p < 0.001), and social norms for team-sport athletes predicted concussion reporting intentions (p < 0.001). Qualitative findings highlighted the important role that coaches and teammates play in creating an open environment that encourages concussion reporting. Education and awareness were also seen to further develop such an environment, while athletes identified several barriers to reporting symptoms of concussion. Conclusions: The findings from this study provide researchers and practitioners with preliminary evidence that can shape the design of interventions to support the development of a team environment that can promote concussion reporting and, thus, prioritise player healt

miRNA profiling of hiPSC-derived neurons from monozygotic twins discordant for schizophrenia

Schizophrenia is a complex developmental disorder whose molecular mechanisms are not fully understood. The developmental course of schizophrenia can be modeled with human induced pluripotent stem cell (hiPSC) -derived brain cells that carry patient-specific genetic risk factors for the disorder. Although transcriptomic characterization of the patient-derived cells is a standard procedure, microRNA (miRNA) profiling is less frequently performed. To investigate the role of miRNAs in transcriptomic regulation in schizophrenia, we performed miRNA sequencing for hiPSC-derived neurons from five monozygotic twin pairs discordant for schizophrenia and six controls (CTR). We compared the miRNA expression to differentially expressed genes (DEGs) reported for the same cells in our earlier work. We found 21 DEmiRNAs between the affected twins (AT) and CTR with implications for the regulation of neuronal function. In addition, a separate analysis of three AT with treatment-resistant schizophrenia (TRS), their unaffected twins (UT), and CTR revealed an upregulation of four miRNAs in the UT compared to both AT and CTR. The DEmiRNAs found between the UT and CTR were associated with increased cAMP/PKA signaling and synaptogenesis signaling in the UT. We hypothesize that the upregulation of these processes in the UT could be linked to compensatory features against schizophrenia