Journal articles on the topic 'Dropout behavior, Prediction of Australia'
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Chi, Zengxiao, Shuo Zhang, and Lin Shi. "Analysis and Prediction of MOOC Learners’ Dropout Behavior." Applied Sciences 13, no. 2 (January 13, 2023): 1068. http://dx.doi.org/10.3390/app13021068.
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With the wide spread of massive open online courses ( MOOC ), millions of people have enrolled in many courses, but the dropout rate of most courses is more than 90%. Accurately predicting the dropout rate of MOOC is of great significance to prevent learners’ dropout behavior and reduce the dropout rate of students. Using the PH278x curriculum data on the Harvard X platform in spring 2013, and based on the statistical analysis of the factors that may affect learners’ final completion of the curriculum from two aspects: learners’ own characteristics and learners’ learning behavior, we established the MOOC dropout rate prediction models based on logical regression, K nearest neighbor and random forest, respectively. Experiments with five evaluation metrics (accuracy, precision, recall, F1 and AUC) show that the prediction model based on random forest has the highest accuracy, precision, F1 and AUC, which are 91.726%, 93.0923%, 95.4145%, 0.925341, respectively, its performance is better than that of the prediction model based on logical regression and that of the model based on K-nearest neighbor, whose values of these metrics are 91.395%, 92.8674%, 95.2337%, 0.912316 and 91.726%, 93.0923%, 95.4145% and 0.925341, respectively. As for recall metrics, the value of random forest is higher than that of KNN, but slightly lower than that of logistic regression, which are 0.992476, 0.977239 and 0.978555, respectively. Then, we conclude that random forests perform best in predicting the dropout rate of MOOC learners. This study can help education staff to know the trend of learners’ dropout behavior in advance, so as to put some measures to reduce the dropout rate before it occurs, thus improving the completion rate of the curriculum.
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Keijsers, Ger P. J., Mirjam Kampman, and Cees A. L. Hoogduin. "Dropout prediction in cognitive behavior therapy for panic disorder." Behavior Therapy 32, no. 4 (2001): 739–49. http://dx.doi.org/10.1016/s0005-7894(01)80018-6.
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Shou, Zhaoyu, Pan Chen, Hui Wen, Jinghua Liu, and Huibing Zhang. "MOOC Dropout Prediction Based on Multidimensional Time-Series Data." Mathematical Problems in Engineering 2022 (April 28, 2022): 1–12. http://dx.doi.org/10.1155/2022/2213292.
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Massive open online courses have attracted millions of learners worldwide with flexible learning options. However, online learning differs from offline education in that the lack of communicative feedback is a drawback that magnifies high dropout rates. The analysis and prediction of student’s online learning process can help teachers find the students with dropout tendencies in time and provide additional help. Previous studies have shown that analyzing learning behaviors at different time scales leads to different prediction results. In addition, noise in the time-series data of student behavior can also interfere with the prediction results. To address these issues, we propose a dropout prediction model that combines a multiscale fully convolutional network and a variational information bottleneck. The model extracts multiscale features of student behavior time-series data by constructing a multiscale full convolutional network and then uses a variational information bottleneck to suppress the effect of noise on the prediction results. This study conducted multiple cross-validation experiments on KDD CUP 2015 data set. The results showed that the proposed method achieved the best performance compared to the baseline method.
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Albán, Mayra, David Mauricio, and . "Decision Trees for the Early Identification of University Students at Risk of Desertion." International Journal of Engineering & Technology 7, no. 4.44 (December 1, 2018): 51. http://dx.doi.org/10.14419/ijet.v7i4.44.26862.
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The student's dropout at the universities is a topic that has generated controversy in Higher Education Institutions. It has negative effects which cause problems in the social, academic and economic context of the students. One of the alternatives used to predict the dropout at the universities is the implementation of machine learning techniques such as decision trees, known as prediction models that use logical construction diagrams to characterize the behavior of students and identify early students that at in risk of leaving university. Based on a survey of 3162 students, it was possible to obtain 10 variables that have influence into the dropout, that’s why, a CHAID decision tree model is proposed that presents the 97.95% of the accuracy in the prediction of the university students’ dropout. The proposed prediction model allows the administrators of the universities developing strategies for effective intervention in order to establish actions that allow students finishing their university careers successful.
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De Souza, Vanessa Faria, and Gabriela Perry. "Identifying student behavior in MOOCs using Machine Learning." International Journal of Innovation Education and Research 7, no. 3 (March 31, 2019): 30–39. http://dx.doi.org/10.31686/ijier.vol7.iss3.1318.
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This paper presents the results literature review, carried out with the objective of identifying prevalent research goals and challenges in the prediction of student behavior in MOOCs, using Machine Learning. The results allowed recognizingthree goals: 1. Student Classification and 2. Dropout prediction. Regarding the challenges, five items were identified: 1. Incompatibility of AVAs, 2. Complexity of data manipulation, 3. Class Imbalance Problem, 4. Influence of External Factors and 5. Difficulty in manipulating data by untrained personnel.
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Siebra, Clauirton Albuquerque, Ramon N. Santos, and Natasha C. Q. Lino. "A Self-Adjusting Approach for Temporal Dropout Prediction of E-Learning Students." International Journal of Distance Education Technologies 18, no. 2 (April 2020): 19–33. http://dx.doi.org/10.4018/ijdet.2020040102.
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This work proposes a dropout prediction approach that is able to self-adjust their outcomes at any moment of a degree program timeline. To that end, a rule-based classification technique was used to identify courses, grade thresholds and other attributes that have a high influence on the dropout behavior. This approach, which is generic so that it can be applied to any distance learning degree program, returns different rules that indicate how the predictions are adjusted along with academic terms. Experiments were carried out using four rule-based classification algorithms: JRip, OneR, PART and Ridor. The outcomes show that this approach presents better accuracy according to the progress of students, mainly when the JRip and PART algorithms are used. Furthermore, the use of this method enabled the generation of rules that stress the factors that mainly affect the dropout phenomenon at different degree moments.
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Tang, Xingqiu, Hao Zhang, Ni Zhang, Huan Yan, Fangfang Tang, and Wei Zhang. "Dropout Rate Prediction of Massive Open Online Courses Based on Convolutional Neural Networks and Long Short-Term Memory Network." Mobile Information Systems 2022 (May 16, 2022): 1–11. http://dx.doi.org/10.1155/2022/8255965.
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Massive open online courses (MOOC) is characterized by large scale, openness, autonomy, and personalization, attracting increasingly students to participate in learning and gaining recognition from more and more people. This paper proposes a network model based on convolutional neural networks and long short-term memory network (CNN-LSTM) for MOOC dropout prediction task. The model selects 43-dimensional behavioral features as input from students’ learning activity logs and adopts the CNN model to automatically extract continuous features over a period of time from students’ learning activity logs. At the same time, considering the time sequence of students’ learning behavior characteristics, a MOOC dropout prediction model was established by using long short-term memory network to obtain students’ learning status at different time steps. The algorithm proposed in this chapter was trained and evaluated on the public dataset provided by the KDD Cup 2015 competition. Compared with the dropout prediction methods based on LSTM and CNN-RNN, the model improved the AUC by 2.7% and 1.4%, respectively. The result in this paper is a good predictor of dropout rates and is expected to provide teaching aid to teachers.
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Chen, Jing, Jun Feng, Xia Sun, Nannan Wu, Zhengzheng Yang, and Sushing Chen. "MOOC Dropout Prediction Using a Hybrid Algorithm Based on Decision Tree and Extreme Learning Machine." Mathematical Problems in Engineering 2019 (March 18, 2019): 1–11. http://dx.doi.org/10.1155/2019/8404653.
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Massive Open Online Courses (MOOCs) have boomed in recent years because learners can arrange learning at their own pace. High dropout rate is a universal but unsolved problem in MOOCs. Dropout prediction has received much attention recently. A previous study reported the problem of learning behavior discrepancy leading to a wide range of fluctuation of prediction results. Besides, previous methods require iterative training which is time intensive. To address these problems, we propose DT-ELM, a novel hybrid algorithm combining decision tree and extreme learning machine (ELM), which requires no iterative training. The decision tree selects features with good classification ability. Further, it determines enhanced weights of the selected features to strengthen their classification ability. To achieve accurate prediction results, we optimize ELM structure by mapping the decision tree to ELM based on the entropy theory. Experimental results on the benchmark KDD 2015 dataset demonstrate the effectiveness of DT-ELM, which is 12.78%, 22.19%, and 6.87% higher than baseline algorithms in terms of accuracy, AUC, and F1-score, respectively.
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Tamada, Mariela Mizota, Rafael Giusti, and José Francisco de Magalhães Netto. "Predicting Students at Risk of Dropout in Technical Course Using LMS Logs." Electronics 11, no. 3 (February 5, 2022): 468. http://dx.doi.org/10.3390/electronics11030468.
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Educational data mining is a process that aims at discovering patterns that provide insight into teaching and learning processes. This work uses Machine Learning techniques to create a student performance prediction model, using academic data and records from a Learning Management System, that correlates with success or failure in completing the course. Six algorithms were employed, with models trained at three different stages of their two-year course completion. We tested the models with records of 394 students from 3 courses. Random Forest provided the best results with 84.47% on the F1 score in our experiments, followed by Decision Tree obtaining similar results in the first subjects. We also employ clustering techniques and find different behavior groups with a strong correlation to performance. This work contributes to predicting students at risk of dropping out, offers insight into understanding student behavior, and provides a support mechanism for academic managers to take corrective and preventive actions on this problem.
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Shang, Xiaoran, Bangbo Huang, and Hongbin Ma. "Multifeedback Behavior-Based Interest Modeling Network for Adaptive Click-Through Rate Prediction." Mobile Information Systems 2022 (August 29, 2022): 1–9. http://dx.doi.org/10.1155/2022/3529928.
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With the rapid development of the Internet, the recommendation system is becoming more and more important in people’s life. Click-through rate prediction is a crucial task in the recommendation system, which directly determines the effect of the recommendation system. Recently, researchers have found that considering the user behavior sequence can greatly improve the accuracy of the click-through rate prediction model. However, the existing prediction models usually use the user click behavior sequence as the input of the model, which will make it difficult for the model to obtain a comprehensive user interest representation. In this paper, a unified multitype user behavior sequence modeling framework named as MBIN, a.k.a. multifeedback behavior-based Interest modeling network, is proposed to cope with uncertainties in the noisy data. The proposed adaptive model uses deep learning technology, obtains user interest representation through multihead attention, denoises user interest representation using the vector projection method, and fuses the user interests using adaptive dropout technology. First, an interest denoising layer is proposed in the MBIN, which can effectively mitigate the noise problem in user behavior sequences to obtain more accurate user interests. Second, an interest fusion layer is introduced so as to effectively model and fuse various types of interest representations of users to achieve personalized interest fusion. Then, we used auxiliary losses based on behavior sequences to enhance the effect of behavior sequence modeling and improve the effectiveness of user interest characterization. Finally, we conduct extensive experiments based on real-world and large-scale dataset to validate the effectiveness of our approach in CTR prediction tasks.
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Freeman, J., M. Velic, F. Colberg, D. Greenslade, P. Divakaran, and J. Kepert. "Development of a tropical storm surge prediction system for Australia." Journal of Marine Systems 206 (June 2020): 103317. http://dx.doi.org/10.1016/j.jmarsys.2020.103317.
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White, Katherine M., and Melissa K. Hyde. "The Role of Self-Perceptions in the Prediction of Household Recycling Behavior in Australia." Environment and Behavior 44, no. 6 (May 10, 2011): 785–99. http://dx.doi.org/10.1177/0013916511408069.
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Marchant, R., A. Hirst, R. H. Norris, R. Butcher, L. Metzeling, and D. Tiller. "Classification and Prediction of Macroinvertebrate Assemblages from Running Waters in Victoria, Australia." Journal of the North American Benthological Society 16, no. 3 (September 1997): 664–81. http://dx.doi.org/10.2307/1468152.
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Das, Vishal, and Tapan Mukerji. "Petrophysical properties prediction from prestack seismic data using convolutional neural networks." GEOPHYSICS 85, no. 5 (August 17, 2020): N41—N55. http://dx.doi.org/10.1190/geo2019-0650.1.
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We have built convolutional neural networks (CNNs) to obtain petrophysical properties in the depth domain from prestack seismic data in the time domain. We compare two workflows — end-to-end and cascaded CNNs. An end-to-end CNN, referred to as PetroNet, directly predicts petrophysical properties from prestack seismic data. Cascaded CNNs consist of two CNN architectures. The first network, referred to as ElasticNet, predicts elastic properties from prestack seismic data followed by a second network, referred to as ElasticPetroNet, that predicts petrophysical properties from elastic properties. Cascaded CNNs with more than twice the number of trainable parameters as compared to end-to-end CNN demonstrate similar prediction performance for a synthetic data set. The average correlation coefficient for test data between the true and predicted clay volume (approximately 0.7) is higher than the average correlation coefficient between the true and predicted porosity (approximately 0.6) for both networks. The cascaded workflow depends on the availability of elastic properties and is three times more computationally expensive than the end-to-end workflow for training. Coherence plots between the true and predicted values for both cases show that maximum coherence occurs for values of the inverse wavenumber greater than 15 m, which is approximately equal to 1/4 the source wavelength or λ/4. The network predictions have some coherence with the true values even at a resolution of 10 m, which is half of the variogram range used in simulating the spatial correlation of the petrophysical properties. The Monte Carlo dropout technique is used for approximate quantification of the uncertainty of the network predictions. An application of the end-to-end network for prediction of petrophysical properties is made with the Stybarrow field located in offshore Western Australia. The network makes good predictions of petrophysical properties at the well locations. The network is particularly successful in identifying the reservoir facies of interest with high porosity and low clay volume.
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Leote, Ana Carolina, Xiaohui Wu, and Andreas Beyer. "Regulatory network-based imputation of dropouts in single-cell RNA sequencing data." PLOS Computational Biology 18, no. 2 (February 17, 2022): e1009849. http://dx.doi.org/10.1371/journal.pcbi.1009849.
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Single-cell RNA sequencing (scRNA-seq) methods are typically unable to quantify the expression levels of all genes in a cell, creating a need for the computational prediction of missing values (‘dropout imputation’). Most existing dropout imputation methods are limited in the sense that they exclusively use the scRNA-seq dataset at hand and do not exploit external gene-gene relationship information. Further, it is unknown if all genes equally benefit from imputation or which imputation method works best for a given gene. Here, we show that a transcriptional regulatory network learned from external, independent gene expression data improves dropout imputation. Using a variety of human scRNA-seq datasets we demonstrate that our network-based approach outperforms published state-of-the-art methods. The network-based approach performs particularly well for lowly expressed genes, including cell-type-specific transcriptional regulators. Further, the cell-to-cell variation of 11.3% to 48.8% of the genes could not be adequately imputed by any of the methods that we tested. In those cases gene expression levels were best predicted by the mean expression across all cells, i.e. assuming no measurable expression variation between cells. These findings suggest that different imputation methods are optimal for different genes. We thus implemented an R-package called ADImpute (available via Bioconductor https://bioconductor.org/packages/release/bioc/html/ADImpute.html) that automatically determines the best imputation method for each gene in a dataset. Our work represents a paradigm shift by demonstrating that there is no single best imputation method. Instead, we propose that imputation should maximally exploit external information and be adapted to gene-specific features, such as expression level and expression variation across cells.
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Lam, Nelson, and John Wilson. "The New Response Spectrum Model for Australia." Electronic Journal of Structural Engineering, no. 01 (March 28, 2008): 6–24. http://dx.doi.org/10.56748/ejse.8201.
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This paper presents an overview of recent research in Australia into seismic activity and ground motion modelling which has culminated in the development of a new response spectrum model for Australia as featured in the new standard for seismic actions. An important element of the research is the prediction of the displacement demand of small-moderate magnitude earthquakes that are characteristics of the intraplate tectonic environment of Australia. The practical implementation of the response spectrum model is illustrated at the end of the paper with the case-study of a lifeline facility. Advancements in seismic demand is complimented by the accurate assessment of the seismic performance of the structure and their sub-assemblages including those with non-ductile behavior.
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Lim, Eun-Pa, Harry H. Hendon, David L. T. Anderson, Andrew Charles, and Oscar Alves. "Dynamical, Statistical–Dynamical, and Multimodel Ensemble Forecasts of Australian Spring Season Rainfall." Monthly Weather Review 139, no. 3 (March 1, 2011): 958–75. http://dx.doi.org/10.1175/2010mwr3399.1.
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Abstract The prediction skill of the Australian Bureau of Meteorology dynamical seasonal forecast model Predictive Ocean Atmosphere Model for Australia (POAMA) is assessed for probabilistic forecasts of spring season rainfall in Australia and the feasibility of increasing forecast skill through statistical postprocessing is examined. Two statistical postprocessing techniques are explored: calibrating POAMA prediction of rainfall anomaly against observations and using dynamically predicted mean sea level pressure to infer regional rainfall anomaly over Australia (referred to as “bridging”). A “homogeneous” multimodel ensemble prediction method (HMME) is also introduced that consists of the combination of POAMA’s direct prediction of rainfall anomaly together with the two statistically postprocessed predictions. Using hindcasts for the period 1981–2006, the direct forecasts from POAMA exhibit skill relative to a climatological forecast over broad areas of eastern and southern Australia, where El Niño and the Indian Ocean dipole (whose behavior POAMA can skillfully predict at short lead times) are known to exert a strong influence in austral spring. The calibrated and bridged forecasts, while potentially offering improvement over the direct forecasts because of POAMA’s ability to predict the main drivers of springtime rainfall (e.g., El Niño and the Southern Oscillation), show only limited areas of improvement, mainly because strict cross-validation limits the ability to capitalize on relatively modest predictive signals with short record lengths. However, when POAMA and the two statistical–dynamical rainfall forecasts are combined in the HMME, higher deterministic and probabilistic skill is achieved over any of the single models, which suggests the HMME is another useful method to calibrate dynamical model forecasts.
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Tapper, NJ, G. Garden, J. Gill, and J. Fernon. "The Climatology and Meteorology of High Fire Danger in the Northern Territory." Rangeland Journal 15, no. 2 (1993): 339. http://dx.doi.org/10.1071/rj9930339.
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In most areas of Australia the calculation of a fire danger index (FDI) is the cornerstone of fie weather forecasting and provides an operationally objective basis for the issue of fire weather warnings. FDI's are derived from the observation or prediction of a number of basic meteorological parameters which are then combined with information on fuel characteristics. The forest and grassland fire danger in southern Australia is greatest during the austral summer and is characterised by long periods of low fire danger interspersed with occasional extreme fire danger events. By contrast, much of tropical and subtropical Australia shows a distinctly different seasonality, magnitude and frequency of fire danger. The problem is essentially one of the austral winter-spring (dry season) period and is characterised by extended periods of moderate to high fire danger. This paper provides a broad climatological background to the problem of high fire danger in northern Australia, concentrating in particular on the Northern Territory. The paper also addresses particular meteorological situations in northern Australia which give rise to elevated fire danger. Two synoptic-scale weather patterns are discussed in particular; the passage of prefrontal troughs which seasonally produce high fire danger in the region of the tropic, and winter subtropical ridging which produces strong winds and high fire danger over the north of the continent during the dry season.
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Ehsani, Mohammad Reza, Jorge Arevalo, Christoforus Bayu Risanto, Mostafa Javadian, Charles John Devine, Alireza Arabzadeh, Hector L. Venegas-Quiñones, Ambria Paige Dell’Oro, and Ali Behrangi. "2019–2020 Australia Fire and Its Relationship to Hydroclimatological and Vegetation Variabilities." Water 12, no. 11 (November 2, 2020): 3067. http://dx.doi.org/10.3390/w12113067.
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Wildfire is a major concern worldwide and particularly in Australia. The 2019–2020 wildfires in Australia became historically significant as they were widespread and extremely severe. Linking climate and vegetation settings to wildfires can provide insightful information for wildfire prediction, and help better understand wildfires behavior in the future. The goal of this research was to examine the relationship between the recent wildfires, various hydroclimatological variables, and satellite-retrieved vegetation indices. The analyses performed here show the uniqueness of the 2019–2020 wildfires. The near-surface air temperature from December 2019 to February 2020 was about 1 °C higher than the 20-year mean, which increased the evaporative demand. The lack of precipitation before the wildfires, due to an enhanced high-pressure system over southeast Australia, prevented the soil from having enough moisture to supply the demand, and set the stage for a large amount of dry fuel that highly favored the spread of the fires.
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Khanesar, Mojtaba Ahmadieh, Jingyi Lu, Thomas Smith, and David Branson. "Electrical Load Prediction Using Interval Type-2 Atanassov Intuitionist Fuzzy System: Gravitational Search Algorithm Tuning Approach." Energies 14, no. 12 (June 16, 2021): 3591. http://dx.doi.org/10.3390/en14123591.
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Establishing accurate electrical load prediction is vital for pricing and power system management. However, the unpredictable behavior of private and industrial users results in uncertainty in these power systems. Furthermore, the utilization of renewable energy sources, which are often variable in their production rates, also increases the complexity making predictions even more difficult. In this paper an interval type-2 intuitionist fuzzy logic system whose parameters are trained in a hybrid fashion using gravitational search algorithms with the ridge least square algorithm is presented for short-term prediction of electrical loading. Simulation results are provided to compare the performance of the proposed approach with that of state-of-the-art electrical load prediction algorithms for Poland, and five regions of Australia. The simulation results demonstrate the superior performance of the proposed approach over seven different current state-of-the-art prediction algorithms in the literature, namely: SVR, ANN, ELM, EEMD-ELM-GOA, EEMD-ELM-DA, EEMD-ELM-PSO and EEMD-ELM-GWO.
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Malik, Ashish, Philip J. Rosenberger, Martin Fitzgerald, and Louise Houlcroft. "Factors affecting smart working: evidence from Australia." International Journal of Manpower 37, no. 6 (September 5, 2016): 1042–66. http://dx.doi.org/10.1108/ijm-12-2015-0225.
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Purpose The purpose of this paper is to analyse data from the New South Wales Government’s Pilot Programme of establishing Smart Work Hubs (SWHs) for enabling teleworking in two busy commuter corridors. The paper analyses the relationships between various firm, job and personal factors and the perceived value, attitudes and expected usage by users of the SWHs. Design/methodology/approach Employing a cross-sectional survey design, the characteristics, values and attitudes of 117 SWH users were analysed using partial least squares (PLS) method of structural equation modelling (SEM). SEM-PLS approach is considered appropriate especially in prediction-based studies and to estimate an endogenous target construct. Findings Results revealed that perceived SWH value significantly influenced attitude towards the SWH, which then had a significant influence on SWH usage intentions, with personal, job and firm factors also playing a role. Further analysis revealed four variables that significantly influenced the perception of family-value benefits (age, income, hub commute distance, work commute distance), however, there were none that significantly influenced the perception of work benefits. Research limitations/implications The small sample size limits statistical inferences and generalisations to be drawn. Further, this paper also discusses how the low and uneven uptake of teleworking at a SWH raises several managerial and policy implications needing attention. Originality/value To the best of the authors’ knowledge, this is the first empirical study analysing the expected values, attitudes and usage intentions of teleworkers in a SWH context. This study adds to the emerging body of human resource management studies on an outward-looking approach. The novel context will provide a useful base for subsequent studies.
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Doody, J. Sean, Colin McHenry, Mike Letnic, Corinne Everitt, Graeme Sawyer, and Simon Clulow. "Forecasting the spatiotemporal pattern of the cane toad invasion into north-western Australia." Wildlife Research 45, no. 8 (2018): 718. http://dx.doi.org/10.1071/wr18091.
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Context The toxic cane toad (Rhinella marina) has invaded over 50 countries and is a serious conservation issue in Australia. Because the cane toad has taken several decades to colonise northern Australia, due to the large size of the continent and the east–west invasion axis, there is scope for making testable predictions about how toads will invade new areas. The western toad invasion front is far from linear, providing clear evidence for heterogeneity in invasion speed. Aims Several ad hoc hypotheses have been offered to explain this heterogeneity, including the evolution of traits that could facilitate dispersal, and spatial heterogeneity in climate patterns. Here an alternative hypothesis is offered, and a prediction generated for the spatiotemporal pattern of invasion into the Kimberley Region – the next frontier for the invading toads in Australia. Methods Using observations of spatiotemporal patterns of cane toad colonisation in northern Australia over the last 15 years, a conceptual model is offered, based on the orientation of wet season river flows relative to the invasion axis, as well as toad rafting and floating behaviour during the wet season. Key results Our model predicts that toads will invade southern areas before northern areas; an alternative model based on rainfall amounts makes the opposite prediction. The models can now be tested by monitoring the spread of invasion front over the next 5–10 years. Conclusions Our conceptual models present a pleuralistic approach to understanding the spatiotemporal invasion dynamics of toads; such an approach and evaluation of the models could prove useful for managing other invasive species. Implications Although control of cane toads has largely proved ineffective, knowledge of the spatiotemporal pattern of the toad invasion in the Kimberley could: (1) facilitate potential management tools for slowing the spread of toads; (2) inform stakeholders in the local planning for the invasion; (3) provide researchers with a temporal context for quantifying toad impacts on animal communities; and (4) reveal the mechanism(s) causing the heterogeneity in invasion speed.
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He, Yanbai, Rui Chen, Xinya Li, Chuanyan Hao, Sijiang Liu, Gangyao Zhang, and Bo Jiang. "Online At-Risk Student Identification using RNN-GRU Joint Neural Networks." Information 11, no. 10 (October 9, 2020): 474. http://dx.doi.org/10.3390/info11100474.
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Although online learning platforms are gradually becoming commonplace in modern society, learners’ high dropout rates and serious academic performance require more attention within the virtual learning environment (VLE). This study aims to predict students’ performance in a specific course as it is continuously running, using the statistic personal biographical information and sequential behavior data with VLE. To achieve this goal, a novel recurrent neural network (RNN)-gated recurrent unit (GRU) joint neural network is proposed to fit both static and sequential data, where the data completion mechanism is also adopted to fill the missing stream data. To incorporate the sequential relationship of learning data, three kinds of time-series deep neural network algorithms: simple RNN, GRU, and LSTM are first taken into consideration as baseline models. Their performances are compared in identifying at-risk students. Experimental results on Open University Learning Analytics Dataset (OULAD) show that simple methods like GRU and simple RNN have better results than the relatively complex LSTM model. The results also reveal that different models have different peak performance time, which results in the proposed joint model that achieves over 80% prediction accuracy of at-risk students at the end of the semester.
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Yin, Hua, Hong Wu, and Sang-Bing Tsai. "Innovative Research on the Construction of Learner’s Emotional Cognitive Model in E-Learning by Big Data Analysis." Mathematical Problems in Engineering 2021 (October 25, 2021): 1–9. http://dx.doi.org/10.1155/2021/1460172.
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This article first addresses the problem that the unstructured data in the existing e-learning education data is difficult to effectively use and the problem that the coarser granularity of sentiment analysis results in traditional sentiment analysis methods and proposes multipolarized sentiment based on fine-grained sentiment analysis evaluation model. Then, an algorithm for behavior prediction and course recommendation based on emotional change trends is proposed, and the established multiple linear regression equation is solved with an improved algorithm. Finally, the method in this paper is verified by a comprehensive example with algorithm comparison analysis and cross-validation evaluation method. The research method proposed in this article provides new research ideas for evaluating and predicting the learning behavior of e-learners, which is conducive to timely discovering learners’ dropout tendency and recommending relevant courses of interest to improve their graduation rate, so as to optimize the learning experience of learners, promote the development of personalized education and effective teaching of the e-learning teaching platform, and provide a certain reference value for accelerating the reform process of education informatization. In order to improve the speed of searching for parameters and the best parameters, this paper proposes a particle swarm algorithm (to improve the support vector machine parameters in a sense) and finds the best parameters which also achieved the goal from academic expression to academic performance.
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Child, Travis, Benjamin L. Phillips, and Richard Shine. "Does desiccation risk drive the distribution of juvenile cane toads (Bufo marinus) in tropical Australia?" Journal of Tropical Ecology 25, no. 2 (March 2009): 193–200. http://dx.doi.org/10.1017/s0266467408005695.
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Abstract:Immediately after their transition from aquatic to terrestrial life, juveniles of many anuran species are restricted to the margins of natal ponds. Understanding the factors determining the duration of that pondside aggregation has direct management ramifications in the case of the invasive cane toad (Bufo marinus) in tropical Australia. Previous work suggests that dispersal confers biotic advantages (reduced risk of cannibalism, enhanced feeding opportunities) to juvenile toads, but desiccation risk constrains these small animals to the moist margins of the pond. If so, juvenile dispersal should be sensitive to fluctuating hydric conditions on a diel and seasonal cycle. We tested this prediction with field observations (monitoring of dispersal to and from the pond) and field experiments (manipulating hydric regimes). Our results support a dynamic model of juvenile distribution, with a primary role for temporal variations in desiccation risk as the primary factor driving dispersal. During the dry season, strong diel cycles in desiccation risk generate a ‘tidal’ flow of juveniles, dispersing out in the moist morning but retreating to the pond margins at midday. Dispersal rates were enhanced by artificial watering during the dry season, and by the onset of the wet season.
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Boys, Craig A., Thomas S. Rayner, Simon M. Mitrovic, Katherine E. Doyle, Lee J. Baumgartner, and John D. Koehn. "Mass fish kills catalyse improved water and fisheries management." Marine and Freshwater Research 73, no. 2 (2022): i. http://dx.doi.org/10.1071/mf21346.
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Mass fish kills capture the world’s attention and their frequency is increasing worldwide. The sudden death of many millions of native fish in the Darling–Baaka River in Australia in 2018–19 was a catalyst for the 11 articles in this special issue. Collectively, they advance our understanding of how to manage these events, dealing with: ecological impacts and recovery; technologies and approaches for prediction, preparedness and response; and the role of the public in preparing and responding to these catastrophic events.
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Luykx, Peter. "A cytogenetic survey of 25 species of lower termites from Australia." Genome 33, no. 1 (February 1, 1990): 80–88. http://dx.doi.org/10.1139/g90-013.
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A survey of 25 species of lower termites (families Mastotermitidae, Termopsidae, and Kalotermitidae) in Australia revealed that centric fusions are a common theme in karyotype evolution in these insects. All but one of the species studied have a basic XX/XY mechanism of sex determination, secondarily complicated in about a third of a species by centric fusions between autosomes and sex chromosomes. There is no obvious relationship between systematic position and presence or absence of these fusions. Fusions between Y chromosomes and autosomes were more common than fusions between X chromosomes and autosomes, in accord with the prediction of the hypothesis that differential selection between the two sexes is the basis for the spread of sex-linked fusions. The absence of these fusions in many species does not favor the idea that a high degree of sex linkage is a necessary condition for the establishment or maintenance of eusocial behavior in termites. The difference in the mechanism of sex determination from that of cockroaches (XX/XO) argues against the evolutionary derivation of termites from ancestral cockroaches; derivation of both groups from some common ancestor with XX/XY sex determination is more likely.Key words: termites, karotype, evolution, sex chromosomes, Australia.
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Chessman, Bruce C. "Prediction of riverine fish assemblages through the concept of environmental filters." Marine and Freshwater Research 57, no. 6 (2006): 601. http://dx.doi.org/10.1071/mf06091.
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Although the taxonomic composition and richness of fish assemblages are important properties to be considered in freshwater bioassessment, conservation and rehabilitation, it can be difficult to establish a natural benchmark for these properties because of widespread human impact and a lack of pristine reference sites or pre-impact data. As an alternative to the reference site approach, the concept of multiple environmental filters was used to predict the assemblages of fish taxa expected in the absence of anthropogenic stress at 85 sites on rivers in north-eastern New South Wales, Australia. The predicted native fish assemblages were compared with the assemblages recorded by backpack and boat electrofishing at each site. The number of native species predicted by the filters model at each site was highly correlated with the observed number of native species (R2 = 0.75; P < 0.001) but the observed number was generally lower. The model had an average sensitivity of 93% and specificity of 87%, but sensitivity and specificity were considerably less for a few species, including some that are known to have suffered historical declines or been translocated outside of their natural ranges. Comparisons between predicted and observed richness and composition can be used to identify areas of high conservation value and areas where native fish assemblages have been adversely affected by anthropogenic impacts.
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So, Chi Chiu, Tsz On Li, Chufang Wu, and Siu Pang Yung. "Differential Spectral Normalization (DSN) for PDE Discovery." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 11 (May 18, 2021): 9675–84. http://dx.doi.org/10.1609/aaai.v35i11.17164.
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Partial differential equations (PDEs) play a prominent role in many disciplines for describing the governing systems of interest. Traditionally, PDEs are derived based on first principles. In the era of big data, the needs of uncovering PDEs from massive data-set are emerging and become essential. One of the latest advance in PDE discovery models is PDE-Net, which has shown promising predictive power with its moment-constrained convolutional filters, but may suffer from noisy data and numerical instability intrinsic in numerical differentiation. We propose a novel and robust regularization method tailored for moment-constrained convolutional filters, namely, Differential Spectral Normalization (DSN), to allow accurate estimation of coefficient functions and stable prediction of dynamics in a long time horizon. We investigated the effectiveness of DSN against batch normalization, dropout, spectral normalization, weight decay, weight normalization, jacobian regularization and orthonormal regularization and supported with empirical evidence that DSN owns the highest effectiveness by learning the convolutional filters in a robust manner. Numerical experiments further reveal that with DSN there is a substantial potential to uncover the hidden PDEs in a scarce data setting and predict the dynamical behavior for a long time horizon, even in a noisy environment where all data samples are contaminated with noise.
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Wright, Boyd R., Donald C. Franklin, and Roderick J. Fensham. "The ecology, evolution and management of mast reproduction in Australian plants." Australian Journal of Botany 70, no. 8 (December 20, 2022): 509–30. http://dx.doi.org/10.1071/bt22043.
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Australia is home to a diverse assemblage of plant species that display marked population-level variation in inter-annual flower or seed output (i.e. masting). These include a semelparous bamboo with an estimated inter-crop period of 40–50 years, numerous iteroparous masting gymnosperms, angiosperms that include landscape-dominant eucalypts, arid-zone wattles and spinifex (Triodia spp.) grasses, and a rich selection of species that display disturbance-related forms of masting such as pyrogenic flowering and environmental prediction. Despite the prevalence of masting in the Australian flora, there has been a paucity of research on these plants. Nevertheless, from the literature available, it appears that, similar to other parts of the world, a continuum of inter-year reproductive variability exists, with a small number of species displaying extreme–high inter-annual seeding variability. From experimental studies and many anecdotal reports, most of the fitness benefits associated with masting evident overseas also operate in Australia (e.g. predator satiation, improved pollination efficiency, and environmental prediction). Additionally, some Australian masting species offer periodically important food resources for Aboriginal nations in the form of seed or fruit. These include the bunya pine (Araucaria bidwillii), members of the cycad genera Cycas and Macrozamia, spinifex (Triodia) grasses, and mulga shrubs (Acacia aneura). Key future research areas for effective conservation of Australian masting plants include (1) improved understanding of how management interventions such as burning and silvicultural thinning influence regeneration dynamics and higher-order trophic interactions, (2) further longitudinal monitoring across a range of habitats to identify other, as yet unknown, species that display reproductive intermittency, and (3) elucidation of how changes to temperature, precipitation and fire regimes under climate change will affect reproduction and regeneration dynamics of the Australian masting flora.
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Prendergast, HDV, and PW Hattersley. "Distribution and cytology of Australian Neurachne and its allies (Poaceae), a group containing C3, C4 and C3-C4 Intermediate species." Australian Journal of Botany 33, no. 3 (1985): 317. http://dx.doi.org/10.1071/bt9850317.
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Cytological, phytogeographical and habitat data are presented for the Neurachneae (Poaceae), a tribe endemic to Australia and containing seven C3 two C4 and one C3-C4 intermediate species. Chromosome counts for 34 accessions Australia-wide reveal a typical eu-panicoid base number (x = 9). Three species are diploid (Neurachne tenuifolia C3, Thyridolepis mitchelliana C3 and T. xerophila C3,); four species (Paraneurachne muelleri C4, N. minor C3-C4, N. lanigera C3, T. multiculmis C3) are tetraploid only, one (N. queenslandica C3) is hexaploid only, while two (N. alopecuroidea C3 and N. munroi C4) are variable. Aneuploidy was found in individuals of N. minor (2n = 4x+1) and N. queenslandica (2n = 6x -1). Chromosomes are small (mean c. 2 �m) and metacentric or submetacentric. Using localities derived from all known collections in Australian herbaria, actual and computer-predicted distributions were mapped using the Bioclimate Prediction System (BIOCLIM) developed by H. A. Nix and J. R. Busby. Species distributions, habitats and chromosome counts are discussed in relation to photosynthetic pathway, present and past climates and evolutionary history. The Neurachneae are mainly subtropical, arid and semiarid zone plants. However, the distribution of their C3 species contrasts with those of other C3 eu-panicoids and C3 grasses as a whole. The temperate species N. alopecuroidea is the only native C3 eu-panicoid known from south-western Australia. It is suggested that phenotypic expression of C4, photosynthesis in the Neurachneae occurred independently of other grasses and that they did not extend into arid and semiarid regions from a mesic temperate zone.
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Southgate, R., G. Allan, and B. Ostendorf. "An examination of the Stafford Smith - Morton ecological model: a case study in the Tanami Desert, Australia." Rangeland Journal 28, no. 2 (2006): 197. http://dx.doi.org/10.1071/rj06022.
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The pattern of substrate, climatic, vegetation and fire features in the Tanami Desert were considered in relation to the ecological model for arid Australia proposed by Stafford Smith and Morton. The nature and accuracy of spatial data used to describe and quantify the pattern of the landscape features were also examined. Components of the ecological model were accurately reflected in the study area. For example, substrates identified as the most productive amounted to less than 8% of the region, and there was substantial spatial and interannual variation in rainfall. However, a strong climatic gradient was also evident in the study area, a feature not accommodated for in the model proposed by Stafford Smith and Morton. Vegetative ground and shrub cover decreased from north to south and was strongly associated with increasing aridity and lower maximum and minimum temperatures. Spinifex (Triodia spp.) cover showed a curvilinear response. The spatial data for both substrate and fire history were reasonably accurate (around 90%) when compared with ground-truthed data, and is considered suitable to reflect ecological pattern and process in the Tanami Desert. Both the adequacy of the ecological model and accuracy of spatial data are important issues to consider before the development of statistical modelling for prediction of species distribution.
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Limpus, CJ, and N. Nicholls. "The Southern Oscillation Regulates the Annual Numbers of Green Turtles (Chelonia-Mydas) Breeding Around Northern Australia." Wildlife Research 15, no. 2 (1988): 157. http://dx.doi.org/10.1071/wr9880157.
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The green turtle (Chelonia mydas) is one of six turtle species which breeds around northern Australia and Indonesia. The number of green turtles observed nesting varies substantially from year to year. The interannual fluctuations in the number of nesting turtles are in phase at widely separated rookeries. They are also correlated with an index of the Southern Oscillation, a coherent pattern of atmospheric pressure, temperature and rainfall fluctuations which dominates the interannual variability of the climate of the tropical Pacific. Major fluctuations in the numbers of turtles breeding occur two years after major fluctuations in the Southern Oscillation. The relationship is strong enough to be useful in predicting, two years in advance, the numbers of green turtles breeding in Great Barrier Reef rookeries. This is the first study to report a biological impact of the Southern Oscillation that allows such a long-range prediction of the impact.
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Wang, Qian, Wenfang Zhao, and Jiadong Ren. "Intrusion detection algorithm based on image enhanced convolutional neural network." Journal of Intelligent & Fuzzy Systems 41, no. 1 (August 11, 2021): 2183–94. http://dx.doi.org/10.3233/jifs-210863.
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Intrusion Detection System (IDS) can reduce the losses caused by intrusion behaviors and protect users’ information security. The effectiveness of IDS depends on the performance of the algorithm used in identifying intrusions. And traditional machine learning algorithms are limited to deal with the intrusion data with the characteristics of high-dimensionality, nonlinearity and imbalance. Therefore, this paper proposes an Intrusion Detection algorithm based on Image Enhanced Convolutional Neural Network (ID-IE-CNN). Firstly, based on the image processing technology of deep learning, oversampling method is used to increase the amount of original data to achieve data balance. Secondly, the one-dimensional data is converted into two-dimensional image data, the convolutional layer and the pooling layer are used to extract the main features of the image to reduce the data dimensionality. Thirdly, the Tanh function is introduced as an activation function to fit nonlinear data, a fully connected layer is used to integrate local information, and the generalization ability of the prediction model is improved by the Dropout method. Finally, the Softmax classifier is used to predict the behavior of intrusion detection. This paper uses the KDDCup99 data set and compares with other competitive algorithms. Both in the performance of binary classification and multi-classification, ID-IE-CNN is better than the compared algorithms, which verifies its superiority.
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Khan, Mansoor, Tianqi Liu, and Farhan Ullah. "A New Hybrid Approach to Forecast Wind Power for Large Scale Wind Turbine Data Using Deep Learning with TensorFlow Framework and Principal Component Analysis." Energies 12, no. 12 (June 12, 2019): 2229. http://dx.doi.org/10.3390/en12122229.
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Wind power forecasting plays a vital role in renewable energy production. Accurately forecasting wind energy is a significant challenge due to the uncertain and complex behavior of wind signals. For this purpose, accurate prediction methods are required. This paper presents a new hybrid approach of principal component analysis (PCA) and deep learning to uncover the hidden patterns from wind data and to forecast accurate wind power. PCA is applied to wind data to extract the hidden features from wind data and to identify meaningful information. It is also used to remove high correlation among the values. Further, an optimized deep learning algorithm with a TensorFlow framework is used to accurately forecast wind power from significant features. Finally, the deep learning algorithm is fine-tuned with learning error rate, optimizer function, dropout layer, activation and loss function. The algorithm uses a neural network and intelligent algorithm to predict the wind signals. The proposed idea is applied to three different datasets (hourly, monthly, yearly) gathered from the National Renewable Energy Laboratory (NREL) transforming energy database. The forecasting results show that the proposed research can accurately predict wind power using a span ranging from hours to years. A comparison is made with popular state of the art algorithms and it is demonstrated that the proposed research yields better predictions results.
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Williams, JB, D. Bradshaw, and L. Schmidt. "Field Metabolism and Water Requirements of Spinifex Pigeons (Geophaps-Plumifera) in Western-Australia." Australian Journal of Zoology 43, no. 1 (1995): 1. http://dx.doi.org/10.1071/zo9950001.
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Spinifex pigeons (Geophaps plumifera) are one of the few avian species that have evolved the capacity to reside in the hot and dry regions of central and north-western Australia. Previous investigation has revealed that their basal metabolic rate (BMR) equals only 68% of allometric prediction. In this study, we addressed the hypothesis that these birds have a reduced field metabolic rate (FMR) and water influx as a result of their lowered BMR. We measured the FMR and water flux of free-living spinifex pigeons by means of the doubly labelled water method. Although body mass of free-living male and female pigeons differed significantly, with males weighing on average 90.8 +/- 7.7 g (+/- s.d.) and females 80.2 +/- 5.6 g, FMR was statistically indistinguishable between sexes. For sexes combined, FMR averaged 139.9 mL CO2 h-1, or 73.5 kJ day-1, a value 38.7% of allometric expectation. These data support the hypothesis that spinifex pigeons have a markedly reduced FMR, probably, in part, the result of a depressed BMR compared with other birds of similar size. Our phylogenetic analysis of the BMR of pigeons lacked sufficient data to determine whether a reduced BMR in Australian pigeons was the consequence of ecological adaptation or phylogenetic constraint. Water influx ranged from 2.5 to 39.0 mL day-1 and averaged 18.4 mL day-1. Of the total water intake, 83.5% came from drinking; their food, seeds, supplied about 4%. Maintenance metabolism, energy allocated to basal plus thermoregulatory metabolism, accounted for about 67% of the average FMR, indicating that the activity requires relatively low energy expenditure in these birds.
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Adnan, Muhammad, Duaa H. AlSaeed, Heyam H. Al-Baity, and Abdur Rehman. "Leveraging the Power of Deep Learning Technique for Creating an Intelligent, Context-Aware, and Adaptive M-Learning Model." Complexity 2021 (July 13, 2021): 1–21. http://dx.doi.org/10.1155/2021/5519769.
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Machine learning (ML) and deep learning (DL) algorithms work well where future estimations and predictions are required. Particularly, in educational institutions, ML and DL algorithms can help instructors in predicting the learning performance of learners. Furthermore, the prediction of the learning performance of learners can assist instructors and intelligent learning systems (ILSs) in taking preemptive measures (i.e., early engagement or early intervention measures) so that the learning performance of weak learners could be increased thus reducing learners’ failures and dropout rates. In this study, we propose an intelligent learning system (ILS) powered by the mobile learning (M-learning) model that predicts learners’ performance and classify them into various performance groups. Subsequently, adaptive feedback and support are provided to those learners who struggle in their studies. Four M-learning models were created for different learners considering their learning features (study behavior) and their weight values. The M-learning model was based on the artificial neural network (ANN) algorithm with the aim to predict learners’ performance and classify them into five performance groups, whereas the random forest (RF) algorithm was used to determine each feature’s importance in the creation of the M-learning model. In the last stage of this study, we performed an early intervention/engagement experiment on those learners who showed weak performance in their study. End-user computing satisfaction (EUCS) model questionnaire was adopted to measure the attitude of learners towards using an ILS. As compared to traditional machine learning algorithms, ANN achieved the highest prediction accuracy for all four learning models, i.e., model 1 = 90.77%, model 2 = 87.69%, model 3 = 83.85%, and model 4 = 80.00%. Moreover, the five most important features that significantly affect the students’ final performance were MP3 = 0.34, MP1 = 0.26, MP2 = 0.24, NTAQ = 0.05, and AST = 0.018.
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Mills, Courtenay E., Wade L. Hadwen, and Jane M. Hughes. "Looking through glassfish: marine genetic structure in an estuarine species." Marine and Freshwater Research 59, no. 7 (2008): 627. http://dx.doi.org/10.1071/mf07215.
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Through the use of mitochondrial DNA (ATP8 gene), the prediction of intermediate genetic structuring was investigated in two species of estuarine glassfish (Ambassis marianus and Ambassis jacksoniensis) (Perciformes : Ambassidae) to determine the possibility of a generalised ‘estuarine’ genetic structure. Individuals were collected from estuaries in eastern Australia between Tin Can Bay (Queensland) in the north and Kempsey (New South Wales) in the south. Analysis of the haplotype frequencies found in this region suggested panmictic populations with star-like phylogenies with extremely high levels of genetic diversity, but with no correlation between geographic distance and genetic distance. Non-significant FST and ΦST suggested extensive dispersal among estuaries. However, Tajima’s D and Fu’s FS values suggest ‘mutation–genetic drift equilibrium’ has not been reached, and that population expansions occurring 262 000 (A. marianus) and 300 000 (A. jacksoniensis) years ago may obscure any phylogeographic structuring or isolation by distance. The finding of panmixia was contrary to the prediction of genetic structuring intermediate between that of marine fish (shallowly structured) and freshwater fish (highly structured), suggesting high dispersal capabilities in these species.
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Ferris, JM, and PA Tyler. "Chlorophyll-total phosphorus relationships in Lake Burragorang, New South Wales, and some other Southern Hemisphere lakes." Marine and Freshwater Research 36, no. 2 (1985): 157. http://dx.doi.org/10.1071/mf9850157.
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Linear regression of chlorophyll concentration on total phosphorus concentration for phosphorus- limited Lake Burragorang, N.S.W., yields regression coefficients within the range reported for individual lakes in the Northern Hemisphere. Some variation in slope of published regressions is attributable to the choice of different regression subvariables (e.g. annual mean or annual maximum). The extent of this variation is quantified. Data from Lake Burragorang and other sites indicate that chlorophyll-phosphorus relationships in the Southern Hemisphere are concordant with those in the north if turbid waters are excluded from consideration. This is obviously significant in Australia, with so many turbid waters. The notion of 'growing season', as applied to Northern Hemisphere studies, is inappropriate for the warm temperate conditions of Lake Burragorang, and it was necessary instead to use the annual maximum chlorophyll concentration. Prediction of annual maximum chlorophyll concentration is of particular significance to water-quality management. Despite highly significant regressions, 95% confidence intervals and 95% prediction limits are wide, so that prediction of chlorophyll concentration from single values of total phosphorus, using double-In regressions, gives a wide arithmetic range. Use of annual mean total phosphorus concentration as the predictor variable limits the forecasting ability of the Lake Burragorang regressions but facilitates future coupling with a phosphorus loading model. This would assist in the assessment of projected management plans and the formulation of protective loading criteria.
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Rees, Michael, David J. Paull, and Susan M. Carthew. "Factors influencing the distribution of the yellow-bellied glider (Petaurus australis australis) in Victoria, Australia." Wildlife Research 34, no. 3 (2007): 228. http://dx.doi.org/10.1071/wr06027.
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In this study we examine broad-scale factors affecting the distribution of the yellow-bellied glider (Petaurus australis australis) in the southern Australian state of Victoria. Using the bioclimatic analysis and prediction system, BIOCLIM, and vegetation-suitability mapping, we assessed the potential distribution of the species at the time of European settlement and compared it to the current distribution. BIOCLIM revealed that P. a. australis is most likely to occur in areas with mean annual rainfall >600 mm and mean annual temperature between 6°C and 14.5°C. Much of its current distribution is skewed to the eastern half of the State, and our results emphasise a disjunction between western and eastern Victorian populations that is attributed to unsuitable climate and vegetation for the species. This indicates that P. australis in the west was most likely separated from eastern Victorian P. australis long before European settlement. Our results also indicate that isolated P. australis populations in south-western Victoria represent fragments of what was probably a much more widely distributed population when European settlement took place. Owing to the highly restricted distribution of suitable remnant native vegetation, these westernmost P. australis populations should be a high priority for future research and conservation work.
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Tibby, John, and Michael A. Reid. "A model for inferring past conductivity in low salinity waters derived from Murray River (Australia) diatom plankton." Marine and Freshwater Research 55, no. 6 (2004): 597. http://dx.doi.org/10.1071/mf04032.
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Detecting human-induced salinisation in rivers and wetlands of the Murray-Darling Basin has proved problematic. A diatom-based model that permits the estimation of past electrical conductivity (EC) from sedimentary diatom sequences has been developed from Murray River planktonic diatoms. Canonical Correspondence Analysis indicates that EC explains the greatest amount of variance in Murray River planktonic diatoms and that its influence is partially independent of that associated with velocity, turbidity, pH and nutrients. A weighted-averaging based model for inferring past EC was therefore derived from the relationship between diatom composition and EC in Murray River plankton samples. The model works well when comparisons are made between measured and diatom-inferred EC determined by jackknifing based leave-one-out computer resampling (r2jack = 0.71, root-mean-square-error of prediction = 115 μS cm−1). Application of the model will enhance understanding of the nature of pre-European variability in electrical conductivity and permit detection of changes in conductivity through the period of European occupation at key sites. Such reconstructions will provide a firm empirical basis for assessing European impact on aquatic ecosystems and a means by which to assess restoration efforts.
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Forero, J. A., M. Bravo, J. Pacheco, J. de Brito, and L. Evangelista. "Fracture Behaviour of Concrete with Reactive Magnesium Oxide as Alternative Binder." Applied Sciences 11, no. 7 (March 24, 2021): 2891. http://dx.doi.org/10.3390/app11072891.
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This research evaluates the fracture behavior of concrete with reactive magnesium oxide (MgO). Replacing cement with MgO is an attractive option for the concrete industry, mainly due to sustainability benefits and reduction of shrinkage. Four different MgO’s from Australia, Canada, and Spain were used in the concrete mixes, as a partial substitute of cement, at 5%, 10%, and 20% (by weight). The fracture toughness (KI) intensity factor and the stress–strain softening parameters of the wedge split test were evaluated after 28 days. The experimental results showed that the replacement of cement with MgO reduced the fracture energy between 13% and 53%. Moreover, the fracture energy was found to be correlated with both compressive strength and modulus of elasticity. A well-defined relationship between these properties is important for an adequate prediction of the non-linear behavior of reinforced concrete structures made with partial replacement of cement with MgO.
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Hallam, Amy, and Jennifer Read. "Do tropical species invest more in anti-herbivore defence than temperate species? A test in Eucryphia (Cunoniaceae) in eastern Australia." Journal of Tropical Ecology 22, no. 1 (December 21, 2005): 41–51. http://dx.doi.org/10.1017/s0266467405002919.
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Tropical plants have been suggested to have higher levels of mechanical, chemical and biotic defences than temperate plants. However, comparisons have usually included deciduous species within the temperate group, which confounds the analysis since deciduous species are predicted to have a different strategy with respect to investment, nutrition and defence than evergreen species. In this study we examined levels of defence and nutrition in five evergreen species of Eucryphia occurring along a latitudinal gradient in eastern Australia, grown under common conditions in a glasshouse. From the resource-availability hypothesis we predicted the opposite gradient in defence investment, i.e. that lowest levels of defence will occur in tropical species with potentially high growth rates and annual productivity. However, we found an increase in cell wall content, total phenolics and tannin activity, and a decrease in protein availability, with decreasing latitude and/or increasing mean annual temperature. Hence, there was a trend of increasing defence (although not in leaf toughness) and declining nutritional quality towards the tropics. These latitudinal trends were recorded in both mature and expanding leaves. The same trends were observed in leaves of two species collected from the field, indicating that the results were not peculiar to the experimental growth regime. The latitudinal trend in defence did not support our prediction based on the resource availability hypothesis and may indicate that herbivore pressure is providing an overriding selection pressure, although there are alternative explanations.
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Praveena, T. Lakshmi, and N. V. Muthu Lakshmi. "Perception of Autism Spectrum Disorder Children by Envisaging Emotions from the Facial Images." International Journal of Engineering and Advanced Technology 10, no. 2 (December 30, 2020): 1–5. http://dx.doi.org/10.35940/ijeat.b1960.1210220.
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Image processing is a rapidly growing technology and is one among the thrust areas of research in Medical Fields, various Engineering disciplines, life Sciences and Scientific applications. Many technical applications have already adopted image processing and it plays a key role in predicting unknown or hidden facts easily and efficiently. Facial image processing is an innovative application of image processing and is being widely used in many applications successfully. Some of the applications are used for person identification, identifying authorized persons, identifying criminals and so on. As we all know that person’s emotion shows personality & behavior, moods where he or she expresses feelings by emotions maximum on face only. Facial expression can also be used in various fields like emotion recognition, market analysis, prediction neurological disorder percentage, psychological problems and so on. So, it has become an emerging research area to study. Neurological disorder is a more complicated disease because it affects both physical body and mental body. In this paper a new methodology is proposed using optimized deep learning methods to predict ASD in children of age 1 to 10 years. Proposed model performance is tested on ASD children and normal children facial image dataset collected from Kaggle datasets and also tested on dataset collected from autism parents’ face book group. Convolutional Neural Networks (CNN) is applied on extracted face landmarks using optimization techniques, dropout, batch normalization and parameter updating. Most significant six types of emotions are considered for analysis in predicting ASD children accurately.
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Colombelli-Négrel, Diane, and Sonia Kleindorfer. "In superb fairy wrens (Malurus cyaneus), nuptial males have more blood parasites and higher haemoglobin concentration than eclipsed males." Australian Journal of Zoology 56, no. 2 (2008): 117. http://dx.doi.org/10.1071/zo07072.
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Blood parasites rupture mature red blood cells and so reduce haemoglobin concentration and hence the potential activity levels of infected males. We examined blood parasites and haemoglobin concentration in the superb fairy-wren (Malurus cyaneus) across three years and six locations in South Australia. We tested the prediction that males in nuptial plumage have more blood parasites and hence lower haemoglobin concentration than males in eclipsed plumage. Of 188 birds, 20 (10.6%) had blood parasites (Haemaproteus spp). We found that (1) there was an effect of season and sex on haemoglobin concentration; (2) there was no effect of haemoglobin concentration on prevalence of blood parasites or intensity; and (3) males in nuptial plumage had more blood parasites but higher haemoglobin concentration than eclipsed males.
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Kafle, Bidur, Lihai Zhang, Priyan Mendis, Nilupa Herath, Maizuar Maizuar, Colin Duffield, and Russell G. Thompson. "Monitoring the Dynamic Behavior of The Merlynston Creek Bridge Using Interferometric Radar Sensors and Finite Element Modeling." International Journal of Applied Mechanics 09, no. 01 (January 2017): 1750003. http://dx.doi.org/10.1142/s175882511750003x.
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Bridges play an important role in economic development and bring important social benefits. The development of innovative bridge monitoring techniques will enable road authorities to optimize operational and maintenance activities for bridges. However, monitoring the dynamic behavior of a bridge requires a comprehensive understanding of the interaction between the bridge and traffic loading which has not been fully achieved so far. In the present study, an integrated bridge health monitoring framework is developed using advanced 3D Finite Element modeling in conjunction with Weight-in-motion (WIM) technology and interferometric radar sensors (IBIS-S). The realistic traffic loads imposed on the bridge will be obtained through calibration and validation of traffic loading prediction model using real-time bridge dynamic behavior captured by IBIS-S and WIM data. Using the Merlynston Creek Bridge in Melbourne, Australia as a case study, it demonstrated that the proposed bridge monitoring framework can both efficiently and accurately capture the real-time dynamic behavior of the bridge under traffic loading as well as the dynamic characteristics of the bridge. The outcomes from this research could potentially enhance the durability of bridges which is an important component of the sustainability of transport infrastructure.
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Williams, Craig R., Scott A. Ritchie, and Peter I. Whelan. "Potential distribution of the Asian disease vector Culex gelidus Theobald (Diptera: Culicidae) in Australia and New Zealand: a prediction based on climate suitability." Australian Journal of Entomology 44, no. 4 (November 2005): 425–30. http://dx.doi.org/10.1111/j.1440-6055.2005.00502.x.
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Scott, John K., Kathryn L. Batchelor, and Bruce L. Webber. "Long term monitoring of recruitment dynamics determines eradication feasibility for an introduced coastal weed." NeoBiota 50 (September 2, 2019): 31–53. http://dx.doi.org/10.3897/neobiota.50.35070.
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Bitou bush (Chrysanthemoides monilifera subsp. rotundata) is a Weed of National Significance in Australia and has impacted a significant portion of the eastern coastline. Its discovery in Western Australia was, therefore, a cause for concern. Assessment and control of the isolated and well-defined population began in 2012. To assess the feasibility of eradication in Western Australia as a management outcome for bitou bush, we applied a rigorous data-driven quantification and prediction process to the control program. Between 2012 and 2018 we surveyed over 253 ha of land and removed 1766 bitou bush plants. Approximately 97 person-days were spent over the six years of survey. We measured the seed bank viability for five years starting in 2013, with the 2017 survey results indicating a decline of mean viable seeds/m2 from 39.3 ± 11.4 to 5.7 ± 2.2. In 2018 we found only ten plants and no newly recruited seedlings in the population. No spread to other areas has been recorded. Soil core studies indicate that the soil seed bank is unlikely to persist beyond eight years. Eradication of the population in Western Australia, defined as five years without plants being detected, therefore remains a realistic management goal. The information generated from the documentation of this eradication program provides invaluable insight for weed eradication attempts more generally: novel detection methods can be effective in making surveys more efficient, all survey methods are not entirely accurate and large plants can escape detection, bitou bush seeds persist in the soil but become effectively undetectable at low densities, and migration of seed was unquantifiable, possibly compromising delimitation. Continued monitoring of the Western Australian population will determine how much of a risk these factors represent to eradication as the outcome of this management program.
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Pietras, Marcin. "First record of North American fungus Rhizopogon pseudoroseolus in Australia and prediction of its occurrence based on climatic niche and symbiotic partner preferences." Mycorrhiza 29, no. 4 (June 7, 2019): 397–401. http://dx.doi.org/10.1007/s00572-019-00899-x.
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FULHAM, ELIZABETH, and BARBARA MULLAN. "Hygienic Food Handling Behaviors: Attempting To Bridge the Intention-Behavior Gap Using Aspects from Temporal Self-Regulation Theory." Journal of Food Protection 74, no. 6 (June 1, 2011): 925–32. http://dx.doi.org/10.4315/0362-028x.jfp-10-558.
Full textAbstract:
An estimated 25% of the populations of both the United States and Australia suffer from foodborne illness every year, generally as a result of incorrect food handling practices. The aim of the current study was to determine through the application of the theory of planned behavior what motivates these behaviors and to supplement the model with two aspects of temporal self-regulation theory—behavioral prepotency and executive function—in an attempt to bridge the “intention-behavior gap.” A prospective 1-week design was utilized to investigate the prediction of food hygiene using the theory of planned behavior with the additional variables of behavioral prepotency and executive function. One hundred forty-nine undergraduate psychology students completed two neurocognitive executive function tasks and a self-report questionnaire assessing theory of planned behavior variables, behavioral prepotency, and intentions to perform hygienic food handling behaviors. A week later, behavior was assessed via a follow-up self-report questionnaire. It was found that subjective norm and perceived behavioral control predicted intentions and intentions predicted behavior. However, behavioral prepotency was found to be the strongest predictor of behavior, over and above intentions, suggesting that food hygiene behavior is habitual. Neither executive function measure of self-regulation predicted any additional variance. These results provide support for the utility of the theory of planned behavior in this health domain, but the augmentation of the theory with two aspects of temporal self-regulation theory was only partially successful.