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Australia is one of the most culturally diverse countries in the world. Since World War II, seven million immigrants from more than 150 countries have settled in Australia. Since that time, Federal governmental changes to its policies on immigration has recognized the importance of cultural diversity in its population. Educational institutions have also responded by initiating a variety of strategies and developing curricula aimed at achieving equitable education and social outcomes and promote the acceptance of people from ethnically diverse backgrounds. This paper first examines how Australia has developed policy that has enabled education to become its third largest export market worth more than AUD$18 billion in 2014-2015. It focuses on some of the state and federal government policies that have encouraged the internationalization within the primary, secondary and tertiary education sectors and then gives an overview of some of the institutional strategies and policies that have been implemented at the Royal Melbourne Institute of Technology (RMIT University) at university level and geospatial science undergraduate discipline level. A range of challenges at the governmental, institutional and discipline level faced by those individual academics wishing to incorporate internationalization into their respective curricula are investigated.

EM27/SUN devices are portable solar-viewing Fourier transform spectrometers (FTSs) that are being widely used to constrain measurements of greenhouse gas emissions and validate satellite trace gas measurements. On a 6-week-long campaign in the summer of 2018, four EM27/SUN devices were taken to five Total Carbon Column Observing Network (TCCON) stations in North America, to measure side by side, to better understand their durability, the accuracy and precision of retrievals from their trace gas measurements, and to constrain site-to-site bias among TCCON sites. We developed new EM27/SUN data products using both previous and current versions of the retrieval algorithm (GGG2014 and GGG2020) and used coincident AirCore measurements to tie the gas retrievals to the World Meteorological Organization (WMO) trace gas standard scales. We also derived air-mass-dependent correction factors for the EM27/SUN devices. Pairs of column-averaged dry-air mole fractions (denoted with an X) measured by the EM27/SUN devices remained consistent compared to each other during the entire campaign, with a 10 min averaged precision of 0.3 ppm (parts per million) for XCO 2 , 1.7 ppb (parts per billion) for XCH 4 , and 2.5 ppb for XCO. The maximum biases between TCCON stations were reduced in GGG2020 relative to GGG2014 from 1.3 to 0.5 ppm for XCO 2 and from 5.4 to 4.3 ppb for XCH 4 but increased for XCO from 2.2 to 6.1 ppb. The increased XCO biases in GGG2020 are driven by measurements at sites influenced by urban emissions (Caltech and the Armstrong Flight Research Center) where the priors overestimate surface CO. In addition, in 2020, one EM27/SUN instrument was sent to the Canadian Arctic TCCON station at Eureka, and side-by-side measurements were performed in March–July. In contrast to the other TCCON stations that showed an improvement in the biases with the newer version of GGG, the biases between Eureka's TCCON measurements and those from the EM27/SUN degraded with GGG2020, but this degradation was found to be caused by a temperature dependence in the EM27/SUN oxygen retrievals that is not apparent in the GGG2014 retrievals. [ABSTRACT FROM AUTHOR]

Access to healthcare, including physiotherapy, is increasingly occurring through virtual formats. At-home adherence to physical therapy programs is often poor and few tools exist to objectively measure participation. The aim of this study was to develop and evaluate the potential for performing automatic, unsupervised video-based monitoring of at-home low-back and shoulder physiotherapy exercises using a mobile phone camera. Joint locations were extracted from the videos of healthy subjects performing low-back and shoulder physiotherapy exercises using an open source pose detection framework. A convolutional neural network was trained to classify physiotherapy exercises based on the segments of keypoint time series data. The model's performance as a function of input keypoint combinations was studied in addition to its robustness to variation in the camera angle. The CNN model achieved optimal performance using a total of 12 pose estimation landmarks from the upper and lower body (low-back exercise classification: 0.995 ± 0.009; shoulder exercise classification: 0.963 ± 0.020). Training the CNN on a variety of angles was found to be effective in making the model robust to variations in video filming angle. This study demonstrates the feasibility of using a smartphone camera and a supervised machine learning model to effectively classify at-home physiotherapy participation and could provide a low-cost, scalable method for tracking adherence to physical therapy exercise programs in a variety of settings. [ABSTRACT FROM AUTHOR]

EM27/SUNs are portable solar-viewing Fourier Transform Spectrometers (FTSs) that are being widely used to constrain measurements of greenhouse gas emissions and validate satellite trace gas measurements. On a six-week-long campaign in the summer of 2018, four EM27/SUNs were taken to five Total Carbon Column Observing Network (TCCON) stations in North America to measure side-by-side to better understand their durability, as well as the accuracy and precision of retrievals from their trace gas measurements and to constrain site-to-site bias among TCCON sites. We developed new EM27/SUN data products using both previous and current versions of the retrieval algorithm (GGG2014 and GGG2020) and used coincident AirCore measurements to tie the gas retrievals to the World Meteorological Organization (WMO) trace gas standard scales. We also derived airmass-dependent correction factors for the EM27/SUNs. Pairs of column-averaged dry-air mole fractions (denoted with an X) measured by the EM27/SUNs remained consistent compared to each other during the entire campaign, with a 10-minute averaged precision of 0.3 ppm for XCO2, 1.7 ppb for XCH4 and 2.5 ppb for XCO. The maximum biases between TCCON stations were reduced in GGG2020 relative to GGG2014 from 1.3 ppm to 0.5 ppm for XCO2 and from 5.4 ppb to 4.3 for XCH4 but increased for XCO from 2.2 to 6.1 ppb. The increased XCO biases in GGG2020 are driven by measurements at sites influenced by urban emissions (Caltech and AFRC) where the priors overestimate surface CO. In addition in 2020, one EM27/SUN instrument was sent to the Canadian Arctic TCCON station at Eureka and side-by-side measurements were performed in March–July. In contrast to the other TCCON stations that showed an improvement in the biases with the newer version of GGG, the biases between Eureka's TCCON measurements and those from the EM27/SUN degraded with GGG2020, but this degradation was found to be caused by a temperature dependence in the EM27/SUN oxygen retrievals that is not apparent in the GGG2014 retrievals. [ABSTRACT FROM AUTHOR]

The Gunbower Yemurriki Map details important information about Barapa Country and Barapa people for the purpose of education. The Barapa are the Traditional Owners of the lands north and south of the Murray River around Cohuna, Australia and are working with natural resource agencies to identify and map cultural assets on traditional lands, particularly in relation to water resources. The Gunbower Yemurriki Map has been developed through participatory cultural mapping processes to demonstrate the community connection to water and the wider cultural landscape. Yemurriki is the Barapa word for Country. The map developed and presented in this study will be used to educate the local non-indigenous community about Barapa cultural values and to act as a teaching aid for younger Barapa people. The map depicts stories, totems, and places identified within the landscape. All the information included is what the Barapa consider public and educational. [ABSTRACT FROM AUTHOR]

Land use changes associated with urbanization often have negative impacts on scenic beauty. In this paper, we explore and compare the visual impact of two different scenarios of urban growth including historical urban growth (HUG) and aesthetically sound urban growth (AUG) with two different categories of height for buildings in the city of Gorgan, Iran. This was done by viewshed and landscape metric analysis of different viewpoints and 3D representation of each scenario. The results show that with the AUG scenario, viewsheds are less impacted by new developments than the HUG scenario in all the viewpoints. It can be concluded that building locations can considerably affect the landscape visibility while building height does not impact to the same degree as location. The results of this research, as a Spatial Decision Support System, would help the managers for better understanding of different patterns of urbanization and its effect on landscape view. [ABSTRACT FROM AUTHOR]