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The Upper Hunter Valley is a major coal mining area containing approximately 40% of the currently identified total coal reserves in New South Wales (NSW), Australia. Due to the ongoing increase in mining activities, PM10 (airborne particles with an aerodynamic diameter of less than 10 micrometres) pollution has become a major air quality concern in local communities. This paper summarises the spatial and temporal variability modes of PM10 pollution in the region, based on long-term multi-site monitoring data and the application of the rotated principal component analysis (RPCA) and wavelet analysis techniques. RPCA identified two distinct air quality clusters/subregions in the valley: one in the west/northwest and the other in the southeast. Wavelet analysis revealed the annual cycle to be the most persistent temporal mode of PM10 variability in both subregions, with intermittent signals also observed at time scales of around 120, 30~90, and under 30 days. How these variation modes are related to the effects of local PM10 emissions and the influence of meteorology at different time scales deserves further attention in future work. The findings will be used in air quality reporting and forecasting in NSW. The methodology and results can also be useful for air quality research in similar regions elsewhere. [ABSTRACT FROM AUTHOR]

Background: Anxiety and depressive disorders typically emerge in adolescence and can be chronic and disabling if not identified and treated early. School-based universal mental health screening may identify young people in need of mental health support and facilitate access to treatment. However, few studies have assessed the potential harms of this approach. This paper examines some of the potential mental health-related harms associated with the universal screening of anxiety and depression administered in Australian secondary schools. Methods: A total of 1802 adolescent students from 22 secondary schools in New South Wales, Australia, were cluster randomised (at the school level) to receive either an intensive screening procedure (intervention) or a light touch screening procedure (control). Participants in the intensive screening condition received supervised self-report web-based screening questionnaires for anxiety, depression and suicidality with the follow-up care matched to their symptom severity. Participants in the light touch condition received unsupervised web-based screening for anxiety and depression only, followed by generalised advice on help-seeking. No other care was provided in this condition. Study outcomes included the increased risk of anxiety, depression, psychological distress, decreased risk of help-seeking, increased risk of mental health stigma, determined from measures assessed at baseline, 6 weeks post-baseline, and 12 weeks post-baseline. Differences between groups were analysed using mixed effect models. Results: Participants in the intensive screening group were not adversely affected when compared to the light touch screening condition across a range of potential harms. Rather, participants in the intensive screening group were found to have a decreased risk of inhibited help-seeking behaviour compared to the light touch screening condition. Conclusions: The intensive screening procedure did not appear to adversely impact adolescents' mental health relative to the light touch procedure. Future studies should examine other school-based approaches that may be more effective and efficient than universal screening for reducing mental health burden among students. Trial registration Australian and New Zealand Clinical Trials Registry (ACTRN12618001539224) https://anzctr.org.au/Trial/Registration/TrialReview.aspx?id=375821. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative framework for wind power prediction that focuses on the future of energy forecasting utilizing intelligent deep learning and strategic feature engineering. This research investigates the application of a state-of-the-art deep learning model for wind energy prediction to make extremely short-term forecasts using real-time data on wind generation from New South Wales, Australia. In contrast with typical approaches to wind energy forecasting, this model relies entirely on historical data and strategic feature engineering to make predictions, rather than relying on meteorological parameters. A hybrid feature engineering strategy that integrates features from several feature generation techniques to obtain the optimal input parameters is a significant contribution to this work. The model's performance is assessed using key metrics, yielding optimal results with a Mean Absolute Error (MAE) of 8.76, Mean Squared Error (MSE) of 139.49, Root Mean Squared Error (RMSE) of 11.81, R-squared score of 0.997, and Mean Absolute Percentage Error (MAPE) of 4.85%. Additionally, the proposed framework outperforms six other deep learning and hybrid deep learning models in terms of wind energy prediction accuracy. These findings highlight the importance of advanced data analysis for feature generation in data processing, pointing to its key role in boosting the precision of forecasting applications. [ABSTRACT FROM AUTHOR]

There is opportunity for universities to actively engage with Aboriginal communities to participate, conduct and ideally lead responsible research that attends to community priorities and issues. The Health from the Grassroots (Grassroots) project sought to address an ongoing mismatch between university-defined and community-defined priorities in rural northern New South Wales (NSW), Australia. Grassroots, led by Aboriginal staff of the University Centre for Rural Health (UCRH), aimed at engaging Aboriginal communities in conversations to inform research priorities. The Grassroots project was a true representation of collaborative research led by and for Aboriginal people. The team designed a local survey conducted yarning sessions with community members and used this information to visualize report findings. Community members were highly engaged in the consultation process and the "rich picture" continues to be used to further conversations about evolving health and research priorities. The Aboriginal research team identified strengths and challenges faced by communities and health services in the region and centred community in decision-making for project design and direction. Challenges encountered include limited resources and time for team members to devote to the project outside of their substantive paid positions. This paper describes the project vision, implementation, and research team lessons in the first years. Meaningful consultation to inform research action arose from the team's deep-rooted relationships and identities as members of the community in which we live and work. Research priorities identified through the Grassroots project have been integrated into the ongoing work of the UCRH. [ABSTRACT FROM AUTHOR]

Although traditional physical models have been used to analyse groundwater systems, the emergence of novel machine learning models can improve the accuracy of the predictions. Deep learning has been prominent in environmental and climate change problems. In this paper, we present a framework for utilising deep learning models to predict groundwater levels based on nearby streamflow and rainfall data. We address the missing data problem using a Bayesian linear regression model within the deep learning framework. Our deep learning framework utilises models such as long-short term memory (LSTM) networks and convolutional neural networks (CNN) for multi-step ahead time series prediction. We examine the fluctuations in groundwater levels at various boreholes located near Middle Creek in New South Wales, Australia. We use the National Collaborative Research Infrastructure Strategy (NCRIS) groundwater database and utilise Bayesian linear regression to impute missing data. We investigate the accuracy of the selected models for individual and regional basins and univariate and multivariate strategies. Our results show that the LSTM-based regional model with multivariate strategy using rainfall data provided the best accuracy. • We predict groundwater levels based on stream flow and rainfall data. • Our framework uses Bayesian data imputation and deep learning for multi-step prediction. • We examine the groundwater fluctuations at various boreholes located in Australia. • Results show a fine-tuned regional model with rainfall data yielded the best accuracy. • We find that LSTM models outperformed CNNs using the multivariate approach. [ABSTRACT FROM AUTHOR]