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This paper presents development of an artificial intelligence (AI)‐based model, genetic expression programming (GEP) to predict long‐term streamflow using large‐scale climate drivers as predictors. GEP is chosen over artificial neural networks (ANNs) model, as ANN is a black‐box model, whereas GEP is able to explain the developed forecast models with mathematical expressions. As a case study, 12 streamflow measuring stations were selected from four different regions of New South Wales (NSW) in eastern Australia. A number of climate indices, Pacific Decadal Oscillation (PDO), Indian Ocean Dipole (IOD), El Nino Southern Oscillation (ENSO) and ENSO Modoki index (EMI), were selected as candidate predictors based on the findings of some preliminary studies. Higher predictabilities of the GEP‐based models are evident from the Pearson correlation (r) values ranging between 0.57 and 0.97, which are mostly about twice the values achieved by multiple linear regression (MLR) models in the preliminary study. Performances of the developed models were assessed using standard statistical measures such as root relative squared error (RRSE), relative absolute error (RAE), root mean square error (RMSE), mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE) and Pearson correlation (r) values. The developed models are able to predict spring streamflow up to 5 months in advance with significantly high correlation values. [ABSTRACT FROM AUTHOR]

Wildfires not only severely damage the natural environment and global ecological balance but also cause substantial losses to global forest resources and human lives and property. Unprecedented fire events such as Australia's bushfires have alerted us to the fact that wildfire prediction is a critical scientific problem for fire management. Therefore, robust, long‐lead models and dynamic predictions of wildfire are valuable for global fire prevention. However, despite decades of effort, the dynamic, effective, and accurate prediction of wildfire remains problematic. There is great uncertainty in predicting the future based on historical and existing spatiotemporal sequence data, but with advances in deep learning algorithms, solutions to prediction problems are being developed. Here, we present a dynamic prediction model of global burned area of wildfire employing a deep neural network (DNN) approach that produces effective wildfire forecasts based on historical time series predictors and satellite‐based burned area products. A hybrid DNN that combines long short‐term memory and a two‐dimensional convolutional neural network (CNN2D‐LSTM) was proposed, and CNN2D‐LSTM model candidates with four different architectures were designed and compared to construct the optimal architecture for fire prediction. The proposed model was also shown to outperform convolutional neural networks (CNNs) and the fully connected long short‐term memory (FcLSTM) approach using the refined index of agreement and evaluation metrics. We produced monthly global burned area spatiotemporal prediction maps and adequately reflected the seasonal peak in fire activity and highly fire‐prone areas. Our combined CNN2D‐LSTM approach can effectively predict the global burned area of wildfires 1 month in advance and can be generalized to provide seasonal estimates of global fire risk. [ABSTRACT FROM AUTHOR]

Background: Non‐nutritive sucking habits, bottle feeding, and facial hyperdivergency have been suggested as the influencing factors of anterior open bite (AOB). There was inconsistent reporting of prevalence and a gap of knowledge in the literature. Aim: The aim of this study was to investigate the prevalence of AOB in 7‐ to 12‐year‐olds, with a special interest to characterise the determinants of this malocclusion. Design: A sample of 203 children aged 7 to 12 years were selected from a primary school in regional Australia. The legal guardian of each child completed a self‐administered questionnaire. Data were assessed using methods of univariate statistics and neural analysis. Results: The prevalence of AOB and thumb sucking was 24.1% and 23.2%, respectively. AOB was associated with the habit (correlation = 0.754) and duration (correlation = 0.574) of thumb sucking. Age, gender, birth order, feeding modality, sleep patterns, history of orthodontic treatment and tonsil, adenoid or grommet surgery, and parents' level of education were not related to AOB (correlation absolute value ≤ 0.474). Conclusions: Thumb sucking, specifically for prolonged duration, increases the risk of development of AOB. Cessation of thumb sucking habits should be encouraged at an early age to avoid the development of AOB. [ABSTRACT FROM AUTHOR]

1. Predicting which species are more likely to invade a region presents significant difficulties to researchers and government agencies. Methods for estimating the risk of establishment are often qualitative and rely on consultation with experts and stakeholders. The inherent subjectivity of this process can lead to ambiguities in any estimate of a species’ risk of establishment. 2. Using global presence/absence data of insect crop pests employed a self-organizing map (SOM) to categorize regions based on similarities in species assemblages. This technique enabled them to generate a list of species and rank them based on an index of the risk of establishment. However, the sensitivity of this risk list to errors in the presence/absence data has never been tested. 3. We evaluated the sensitivity of the SOM method by altering the original presence/absence data by increasing amounts and compared estimates of risk with those generated by a national coordinating body (Plant Health Australia) utilizing expert stakeholder opinion. 4. The risk list was unaffected by alterations of up to 20% of data over all regions. The error rate we detected in the data was within these limits. 5. Comparison with the expert stakeholder methodology revealed significant differences in the estimates of establishment risk. Further analysis of the Australian data revealed that a number of regions with strong trade links to Australia supported species assemblages similar to those in Australia, suggesting they are possible sources of pest species with high probability of establishment. 6. Synthesis and applications. This analysis confirms that the SOM methodology is a robust tool in the quantification of risk of establishment. In addition, SOMs can deliver a level of objectivity, which can complement current consultative processes employed by many biosecurity agencies around the world, providing a better overall assessment of invasion risk. This assessment can inform research and development funding decisions and incursion management plans for both government and host industries. While SOMs are utilized in this work for the prioritization of pest insects they can potentially be applied to any taxa (pest or native) or at any scale in which the data are available. [ABSTRACT FROM AUTHOR]

The complete automation of software tests has been considered to be an unattainable goal. This article discusses the potential to achieve this goal with recent discoveries and innovations in the areas of virtual reality (VR), artificial neural networks (ANNs), and artificial intelligence (AI). In this study, a theoretical proposal is described to integrate the properties of each of these areas using a process of automation of software tests. This process is based on a classification and description of the properties after consulting the literature, interviews, and dialogs with specialists from Australia, the United States, Germany, and Colombia. In addition to the experiences of the researchers, the construction of two tools is proposed: (1) a robot to design and apply functional tests, and (2) a virtual machine to identify errors in the logical structure of the code. Both tools are expected to replace human factors; the advantage is that the first tool identifies procedural flaws and the second errors of operation. [ABSTRACT FROM AUTHOR]