Search

This study explored how specific inquiry-related learning activities were related to student enjoyment of learning science and intended choice of future STEM career. Analysis of Taiwan and Australia PISA 2015 data revealed that three activities, namely, "debating and planning experiments, drawing conclusions and doing hands-on experiments, teachers and students explaining ideas," were significantly related to students' enjoyment of learning science and intended choice of STEM careers in these two countries. Cross-national comparisons indicated that the percentages of inquiry-related activities for high or low scientific competency students in Australia were higher than for Taiwanese students. The activity of "teachers and students explaining ideas" was a significant determinant of enjoyment of learning for Taiwanese high scientific competency students and Australian high and low scientific competency students, while "debating and planning experiments" was a positive determinant for Taiwanese low scientific competency students. High scientific competency students from both Taiwan and Australia demonstrated significantly higher preferences for future STEM careers than low scientific competency students.

For the first time in the tourism and environment literature, this study investigates the CO2 emissions effect of the market diversification of tourist arrivals. Theoretically, tourism market diversification has two opposite potential effects on CO2 emissions, depending on its scale and composition effect. It may increase CO2 emissions by expanding the scale of tourism-related activities or decrease CO2 emissions by expanding the share of developed/less-polluted source countries in the destination. By utilizing the econometrics of time series data, we tested the impact of tourism market diversification on CO2 emissions in Australia over the period 1976–2019. Our findings show that tourism market diversification has pro-CO2 emissions effects in the long run for Australia. These findings contribute to our understanding of the environmental impacts of tourism market diversification and help in policy formulations in Australia. [ABSTRACT FROM AUTHOR]

Traditional methods applied in electricity demand forecasting have been challenged by the course of dimensionality arisen with a growing number of distributed or decentralized energy systems are employing. Without manually operated data preprocessing, classic models are not well-calibrated for their robustness when dealing with the disruptive elements (e.g., demand changes in holidays and extreme weather). Based on the application of big data driven analytics, we propose a novel machine learning method originating from the parallel neural networks for robust monitoring and forecasting power demand to enhance supervisory control and data acquisition for new industrial tendency such as Industry 4.0 and Energy IoT. Through our approach, we generalize the implementation of machine learning by using classic feed-forward neural networks, for parallelization in order to let the proposed method achieve superior performance when dealing with high dimensionality and disruptiveness. With the high-frequency data of consumption in Australia from January 2009 to December 2015, the overall empirical results confirm that our proposed method performs significantly better for dynamic monitoring and forecasting of power demand comparing with the classic methods. [ABSTRACT FROM AUTHOR]

Highlights: [•] Phylogenetic relationships in the Cayratia japonica–Cayratia tenuifolia species complex were resolved. [•] Accessions from Australia and Asia formed distinct monophyletic clades. [•] Details of cryptic divergences and hybridizations in the Asian group were revealed. [•] Major divergences within the Asian clade likely occurred in the Tertiary era. [Copyright &y& Elsevier]

As with many other musical traits, the social environment is a key influence on the development of singing ability. While the familial singing environment is likely to be formative, its role relative to other environmental influences such as training is unclear. We used structural equation modeling to test relationships among demographic characteristics, familial environmental variables (early and current singing with family), vocal training, and singing ability in a large, previously documented sample of Australian twins ( N = 1163). Notably, early singing with family, and to a lesser extent vocal training, predicted singing ability, whereas current singing with family did not. Early familial singing also mediated the relationship between sex and singing ability, with men who sang less with family during childhood showing poorer ability. Bivariate twin models between early familial singing and singing ability showed the phenotypic correlation was largely explained by shared environmental influences. This raises the possibility of a sensitive period for singing ability, with sociocultural expectations around singing potentially differentiating the developmental trajectories of this skill for men and women.

Research has shown that people inaccurately assess their own abilities on self-report measures, including academic, athletic, and music ability. Evidence suggests this is also true for singing, with individuals either overestimating or underestimating their level of singing competency. In this paper, we present the Melbourne Singing Tool Questionnaire (MST-Q), a brief 16-item measure exploring people's self-perceptions of singing ability and engagement with singing. Using a large sample of Australian twins (n = 996), we identified three latent factors underlying MST-Q items and examined whether these factors were related to an objective phenotypic measure of singing ability. The three factors were identified as Personal Engagement, Social Engagement, and Self-Evaluation. All factors were positively associated with objective singing performance, with the Self-Evaluation factor yielding the strongest correlation (r = 0.66). Both the Self-Evaluation factor and a single self-report item of singing ability shared the same predictive strength. Contrary to expectations, our findings suggest that self-evaluation strongly predicts singing ability, and this self-evaluation is of higher predictive value than self-reported engagement with music and singing., (© 2023 The Authors. Annals of the New York Academy of Sciences published by Wiley Periodicals LLC on behalf of The New York Academy of Sciences.)