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Quantitative reasoning as a lens to examine changes in modelling competencies of secondary preservice teachers
Full text linkThis study draws on quantitative reasoning research to explain how secondary mathematics preservice teachers’ (PSTs) modelling competencies changed as they participated in a teacher education programme that integrated modelling experience. Adopting a mixed methods approach, we documented 110 PSTs’ competencies in Vietnam using an adapted Modelling Competencies Questionnaire. The results show that PSTs improved their real-world-problem-statement, formulating-a-model, solving-mathematics, and interpreting-outcomes competencies. Showing their formulating-a-model and interpreting-outcomes competencies, PSTs enhanced their quantitative reasoning by properly interpreting the quantities and their relationships using different representations. In addition, the analysis showed a statistically significant correlation between PSTs’ modelling competencies and quantitative reasoning. Suggestions for programme design to enhance modelling competencies are included. © The Author(s) 2024
How has COVID-19 impacted community mental health clinicians and the delivery of care in Australia
No full textAdvanced machine learning and gene expression programming techniques for predicting CO2-induced alterations in coal strength
Full text linkGiven the growing concern over global warming and the critical role of carbon dioxide (CO2) in this phenomenon, the study of CO2-induced alterations in coal strength has garnered significant attention due to its implications for carbon sequestration. A large number of experiments have proved that CO2 interaction time (T), saturation pressure (P) and other parameters have significant effects on coal strength. However, accurate evaluation of CO2-induced alterations in coal strength is still a difficult problem, so it is particularly important to establish accurate and efficient prediction models. This study explored the application of advanced machine learning (ML) algorithms and Gene Expression Programming (GEP) techniques to predict CO2-induced alterations in coal strength. Six models were developed, including three metaheuristic-optimized XGBoost models (GWO-XGBoost, SSA-XGBoost, PO-XGBoost) and three GEP models (GEP-1, GEP-2, GEP-3). Comprehensive evaluations using multiple metrics revealed that all models demonstrated high predictive accuracy, with the SSA-XGBoost model achieving the best performance (R2—Coefficient of determination = 0.99396, RMSE—Root Mean Square Error = 0.62102, MAE—Mean Absolute Error = 0.36164, MAPE—Mean Absolute Percentage Error = 4.8101%, RPD—Residual Predictive Deviation = 13.4741). Model interpretability analyses using SHAP (Shapley Additive exPlanations), ICE (Individual Conditional Expectation), and PDP (Partial Dependence Plot) techniques highlighted the dominant role of fixed carbon content (FC) and significant interactions between FC and CO2 saturation pressure (P). The results demonstrated that the proposed models effectively address the challenges of CO2-induced strength prediction, providing valuable insights for geological storage safety and environmental applications. Copyright © 2025 The Authors
Investigating the suitability of the forensic mental health nursing clinical reasoning cycle for nurses working in generalist mental health settings
Full text linkThe clinical reasoning cycle was designed to guide nursing care and assist with clinical-reasoning and decision-making. While originally developed with an acute health lens, more recently an adapted version has been created for forensic mental health nurses. It is possible that such a framework may also be helpful for mental health nurses working in generalist settings. This study aimed to explore the utility of the original cycle and adapted forensic version with mental health nurses across the state of Victoria, Australia, to determine if the cycle might be suitable to their practice and if any adaptations were necessary. Eighteen nurses participated in focus groups or interviews to explore both versions of the cycle. Following thematic data analysis from phase one, a Nominal Group Technique was used to facilitate exploration of adaptations. Verbal and written responses were collected and participants (n = 6) voted on changes. Three main themes were interpreted from phase one: (1) the mysterious disappearance of nursing frameworks, (2) the CRC fits with what we do, says what we do, and demonstrates what we do, and (3) The CRC becomes more relevant without the word “forensic” in the title. In the nominal group, consensus was reached on 4 of 10 suggested changes from phase one, and the mental health nursing-clinical reasoning cycle was developed. There was concern that many nurses did not have a framework to guide decision-making, and the newly adapted cycle was seen as offering a way of demonstrating the contribution of mental health nursing care to safe practice. © 2025 John Wiley & Sons Australia, Ltd
Curating electronic health record data to assess causal inference effect of metformin on hypertension population progression to chronic kidney disease
Full text linkCausal inference is a methodology to assess the impact of one variable on another by establishing a cause-and-effect relationship. This paper deals with causal inference analyses using Electronic Health Record (EHR) data from the UK Biobank. Key challenges in this area include data curation, missing data, inconsistencies, and invalid entries, which can affect the reliability of results. In this paper, we focus on dealing with these challenges and illustrate our approach with a case study that examines the effect of a drug, i.e., metformin on the progression to chronic kidney disease (CKD) in patients with hypertension as comorbidity. Our approach involves identifying confounders, including other relevant medications, demographic factors, and clinical characteristics. We use two popular techniques, inverse probability weighting and propensity score matching to evaluate metformin's impact on CKD progression. Our findings highlight the significance of rigorous data preparation and the need for careful methodological choices in conducting causal inference studies. With effective use of EHR data, this paper provides a practical guide for similar analysis, offering an alternative method to understand drug effects and disease progression in clinical research, emphasizing the need to address challenges to avoid misleading conclusions in clinical research. Copyright © 2024 held by the owner/author(s)
Photovoltaic systems : analytic comparison of fuzzy logic and ML methods for applying maximum power tracking systems
No full textIntegration of artificial intelligence (AI) in solar power systems for maximum power point tracking (MPPT) is increasingly popular due to the limitations of traditional MPPT methods in locating the global maximum power point (GMPP) under partial shading conditions. Unlike conventional techniques, AI-based algorithms excel at identifying the GMPP even when multiple local maximum power points (MPPs) exist. Compared to traditional methods, AI-based MPPT techniques like reinforcement learning and fuzzy logic typically offer higher efficiency, reduced steady-state oscillation, and faster convergence but require significant resources and investment. This paper compares two AI-based MPPT methods-Fuzzy Logic and Reinforcement Learning using simulation. Each AI approached its strengths and weaknesses, complicating on optimal method selection. It provided a detailed efficiency comparison of these AI methods by implementing them in a solar power grid system under various environmental conditions. © 2025 IEEE
The prevalence and correlates of depression and anxiety symptoms in older adults receiving in-home aged care : a cross-sectional survey
No full textObjectives: To estimate the prevalence of depression and anxiety symptoms in older Australians accessing in-home aged care, and to identify characteristics associated with symptoms. Methods: A cross-sectional telephone survey with a random sample of in-home aged care clients from a national provider (Silverchain) was conducted between November 2022 and July 2023. The percentage of clients experiencing depression and anxiety symptoms was estimated, weighted to the age and gender of the Silverchain population. Multivariable linear regression was utilised to identify characteristics associated with higher depression and anxiety symptoms. Results: A total of 237 participants completed the survey. Over half (52%) of participants experienced symptoms of depression
Advancing aspect-based sentiment analysis in course evaluation : a multi-task learning framework with selective paraphrasing
Full text linkAspect-based sentiment analysis (ABSA) is essential for extracting valuable perspectives from textual data, particularly within educational contexts where understanding student feedback is vital for course evaluation. Multi-task Learning (MTL) models, which concurrently learn several related tasks, have shown promise in enhancing the performance of ABSA by leveraging shared representations. This research explores using MTL with three prominent pre-trained language models (PLMs): MTL-BERT, MTL-RoBERTa, and MTL-XLNet. To further enhance ABSA performance, we integrate a data augmentation method - selective paraphrasing - with the MTL-based PLMs, including SP-MTL-BERT, SP-MTL-RoBERTa, and SP-MTL-XLNet, aimed at enriching the training dataset without compromising the integrity of aspect terms. Additionally, a nuance control mechanism is integrated into the selective paraphrasing process to preserve sentiment intensity and polarity, ensuring semantic consistency and minimizing unintended sentiment drift in the augmented data. However, the scarcity of diverse and comprehensive training data can hinder the effectiveness of these models. For this study, we develop a custom academic dataset by collecting student feedback data on course evaluations comprising more than 11,000 comments from a public university. We conducted experiments on MTL-based models for the two sub-tasks of ABSA, aspect extraction, and sentiment classification, both with and without the integration of selective paraphrasing. The experimental findings indicate that our approach substantially improves performance across all models. Specifically, the SP-MTL-BERT model achieved the highest performance, showing an improvement of +11.0 in aspect recall, +10.8 in aspect F1-score, and +3.7 in sentiment precision, establishing it as the best-performing model in our study. Moreover, comparative analysis with baseline approaches and other data augmentation techniques, such as back translation and easy data augmentation, demonstrates the superior performance of our proposed approach, particularly in improving aspect classification recall and F1 scores. © 2025 IEEE
TriSafeGuard : a unified framework for mask detection, social distancing, and contactless temperature monitoring
No full textThe COVID-19 pandemic underscored the critical need for intelligent strategies to combat viral transmission. This research unveils “TriSafeGuard,” an innovative, integrated framework designed to oversee essential preventive measures, amalgamating sophisticated deep learning techniques with contactless temperature sensing. Our first approach involves a unique methodology utilizing Stacked Auto Encoder (SAE), which combines Principal Component Analysis (PCA) and Depth-wise Separable Convolutional Neural Network (DWSC-NN). This model is adept at identifying not only the presence of masks on individuals but also assessing their proper positioning. Such a technique achieved an impressive accuracy and F1 score of 94.16% and 96.009%, respectively. Next, we tapped into the capabilities of the YOLOv7 architecture for object detection to measure distances between individuals, ensuring adherence to social distancing norms. Utilizing Manhattan distance calculations in high-dimensional spaces, our framework can accurately evaluate the proximity of individuals within crowds. This application of YOLOv7 showcases the robustness of our proposed Social Distance Detection (SDD) technique in effectively discerning safe distances in diverse scenarios. Lastly, integrating a Raspberry pi 4 with an MLX90614 sensor, our setup ensures non-invasive temperature monitoring, forming a critical line of defense against identifying potentially symptomatic individuals. Taken together, these elements culminate in a holistic solution crucial not just for the prevailing pandemic but also as a preparative measure for potential future outbreaks. This paper exemplifies how a synergistic blend of technology can pave the way for a safer, fortified future. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025
“It’s all in your head” : personality traits and gaslighting tactics in intimate relationships
Full text linkBackground: Gaslighting is a form of psychological/emotional abuse inflicted upon an intimate partner that includes manipulative tactics such as misdirection, denial, lying, and contradiction – all to destabilize the victim/survivor. Compared to other forms of intimate partner abuse, gaslighting remains underexplored in the literature. Aims/Purpose: In this preregistered study, we aimed to explore correlates between the Dark Tetrad traits (i.e., grandiose narcissism, vulnerable narcissism, Machiavellian tactics, Machiavellian views, primary psychopathy, secondary psychopathy, and sadism) and acceptance of gaslighting tactics in intimate relationships. Method: Participants (N = 315; Mage = 42.39; 62.2% women) were recruited online and completed an online questionnaire. We developed and internally validated the Gaslighting Questionnaire, a 10-item self-report measure of acceptance of gaslighting tactics in intimate relationships. Results: All the Dark Tetrad traits were associated with more acceptance of gaslighting tactics in intimate relationships, with primary psychopathy, Machiavellian tactics, and sadism emerging as significant predictors in the regression. We also examined sex differences. Compared to women, men found deploying gaslighting tactics more acceptable, and this was largely driven by sex differences in primary psychopathy. Further, men high in vulnerable narcissism demonstrated the greatest acceptance of gaslighting tactics. Conclusions: These findings provide foundational information for understanding gaslighting tactics in intimate partner abuse and may have practical implications for relationship counsellors and clinicians practicing in this space. For example, the present findings indicate that personality assessment can be a valuable tool for estimating a client’s propensity to gaslight. © The Author(s) 2023