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Numerical analysis of Platanus orientalis L. microfiber inhalation exposure in human sinonasal cavities
No full textmiRNA profiling of hiPSC-derived neurons from monozygotic twins discordant for schizophrenia
Full text linkSchizophrenia 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
Early Leak and Burst Detection in Water Pipeline Networks Using Machine Learning Approaches
Full text linkLeakages 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
“Going around the long route to get where I want to be”: Exploring the university experiences of care leaver students
Full text linkThere is an evident, systemic lack of understanding regarding the university success of care leaver students. The few studies in this area indicate that care leavers tend to be highly independent, motivated, and determined to academically succeed, yet also more likely to face compound disadvantage that can impact their participation and completion. The current study sought to better understand the perceived support needs of care leaver university students, with the goal of improving support offerings for future cohorts of care leavers. Semi-structured interviews with seven female care leavers enrolled at an Australian university between 2018–2022 (at time of interview, Mage = 22, enrolled = 5, withdrawn = 2) explored university experiences, perceptions of available supports, enablers and barriers of course completion, as well as recommendations to support future care leaver cohorts. A reflexive thematic analysis revealed participants were required to navigate a university landscape that did not always account for their needs. They reported utilising new relationships and existing internal resources to overcome the unique challenges they encountered. The tension between feeling unable to self-disclose their care leaver status and yet longing to connect with other care leavers presented as a key finding. The findings demonstrated the various supports higher education institutions could introduce such as social opportunities with other care leavers to empower care leaver students in succeeding at university
Hydrochar-nanocomposite membrane combined hydrothermal pretreatment for nutrient upcycling from anaerobic digestate
No full textA new factorisation scheme for the auxiliary particle filter to mitigate the effect of improper second stage weighting
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Advancing Building and Construction Higher Education: The Online Real-Time Block Model’s Contributions to Professional Skills, Gender Equity, and Industry Preparedness
Full text linkTraditional In-Person Semester-Length (IP-SL) courses often struggle with inherent time constraints, lack of flexibility, and geographic limitations, delaying effective learning and accessibility for students. Moreover, the extended duration of the Semester-Length (SL) structure reduce focus due to engagement with multiple subjects simultaneously, increased stress, and limited timely feedback and assessment. This study evaluates the Online Real-Time Block Model (ORT-BM), an intensive online model, highlighting its potential to enhance engagement, satisfaction, and inclusivity in project-based programs like construction in higher education. Building surveying as a critical field in construction is selected as the case study since professional surveyors must stay current with rapidly evolving building codes, regulations, and sustainability practices. However, the rigid structure of IP-SL courses often leaves graduates less prepared to meet industry needs. Conducting a comparative analysis of a case study, the Bachelor of Building Surveying program (NBBS) at Victoria University, the research compares three teaching models: IP-SL (2016–2018), In-Person Block Model (IP-BM, 2019–2020), and ORT-BM (2020–2023) using Student Evaluation of Units (SEU) data and Quality Indicators for Learning and Teaching (QILT) metrics. Findings, derived from SEU and QILT, reveal that ORT-BM improves student satisfaction, accelerates course completion rates, and fosters gender equity through inclusive learning environments while enhancing accessibility for geographically dispersed and disadvantaged students. By integrating advanced digital tools like virtual site visits, ORT-BM enhances professional readiness, aligning education with evolving industry standards. Future research may explore developing hybrid models to optimize cognitive load further, improve accessibility, and enhance flexibility
