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The United Nations Sustainable Development Goal (SDG) number four seeks an equitable and widespread education that enables an outcome of sustainable development by 2030. Intersecting the studies of society and earth processes, a geographical education is well placed to make cohesive sense of all the individual knowledge silos that contribute to achieving sustainability. Geography education is compulsory for the first three years of the secondary education curriculum in Australia; however, research has shown that many geography teachers are underprepared and report limitations in their teaching of sustainability. This article engages with this research problem to provide a critical reflection, using experiential knowledge as an analytical lens, on how tertiary level geography training at one Australian regional university can equip undergraduate teacher education students with the values, knowledge, and skills needed to develop their future students' understanding and appreciation of the principles of sustainability. The authors unpacked a geography minor for a Bachelor of Secondary Education degree at Central Queensland University and, deploying content analysis, explain how three units in that minor can develop these students' values, knowledge, and skills through fostering initiatives and activities. The analysis was framed by elements of pedagogy that offer learners a context for developing active, global citizenship and participation to understand the interdependencies of ecological, societal, and economic systems including a multisided view of sustainability and sustainable development. The study concluded that the three geography units engage student teachers in sustainable thinking in a variety of ways, which can have a wider application in the geography curricula in other teacher education courses. More importantly, however, the study found that there is a critical need for collaboration between university teachers of sustainability content and university teachers of school-based pedagogy in order to maximise the efficacy of sustainability education in schools.

Carbon dioxide (CO2) is one of the main greenhouse gases leading to global warming, and the ocean is the largest carbon reservoir on the earth that plays an important role in regulating CO2 concentration on a global scale. The column-averaged dry-air mole fraction of atmospheric CO2 (XCO2) is a key parameter in describing ocean carbon content. In this paper, the Data Interpolation Empirical Orthogonal Function (DINEOF) and the Bayesian Maximum Entropy (BME) methods are combined to interpolate XCO2 data of Orbiting Carbon Observatory 2 (OCO-2) and Orbiting Carbon Observatory 3 (OCO-3) from January to December 2020 occurring within the geographical range of 15–45°N and 120–150°E. At the first stage of our proposed analysis, spatiotemporal information was used by the DINEOF method to perform XCO2 interpolation that improved data coverage; at the second stage, the DINEOF-generated interpolation results were regarded as soft data and were subsequently assimilated using the BME method to obtain improved XCO2 interpolation values. The performance of the synthetic DINEOF–BME interpolation method was evaluated by means of a five-fold cross-validation method. The results showed that the Mean Absolute Error (MAE), the Root Mean Square Error (RMSE), and the Bias of the DINEOF-based OCO-2 and OCO-3 interpolations were 2.106 ppm, 3.046 ppm, and 1.035 ppm, respectively. On the other hand, the MAE, RMSE, and Bias of the cross-validation results obtained by the DINEOF–BME were 1.285 ppm, 2.422 ppm, and −0.085 ppm, respectively, i.e., smaller than the results obtained by DINEOF. In addition, based on the in situ measured XCO2 data provided by the Total Carbon Column Observing Network (TCCON), the original OCO-2 and OCO-3 data were combined and compared with the interpolated products of the synthetic DINEOF–BME framework. The accuracy of the original OCO-2 and OCO-3 products is lower than the DINEOF–BME-generated XCO2 products in terms of MAE (1.751 ppm vs. 2.616 ppm), RMSE (2.877 ppm vs. 3.566 ppm) and Bias (1.379 ppm vs 1.622 ppm), the spatiotemporal coverage of XCO2 product also improved dramatically from 16% to 100%. Lastly, this study demonstrated the feasibility of the synthetic DINEOF–BME approach for XCO2 interpolation purposes and the ability of the BME method to be successfully combined with other techniques. [ABSTRACT FROM AUTHOR]

Rice is a major food supply in southeast China. With increased population and urbanization, reliable rice mapping is critical in this region. Because of frequent cloud cover and precipitation during the rice-growing season, it is difficult to conduct large-area rice monitoring with optical remote sensing techniques. L-band synthetic aperture radar (SAR), with its all-weather day and night imaging and canopy penetration capabilities, provides a unique alternative. In this study, a first-order radiative transfer model was developed to simulate L-band scattering properties of paddy rice. Three Advanced Land Observing Satellite (ALOS)/Phased-Array-Type L-band Synthetic Aperture Radar (PALSAR) images in dual-polarization mode (HH and HV) acquired in early tillering (June 28, 2007), tillering (August 13, 2007), and heading (September 28, 2007) stages were processed to test the temporal variation of rice backscatter. It was found that plant height and leaf mass amount were the two major structural parameters that contributed to rice backscatter in PALSAR images. The variation of the simulated HH backscatter matched with PALSAR observations in sample fields, although the simulated backscatter coefficients were around 3 dB lower than image-extracted values. Leaf volume scattering and leaf--ground double bounce were found as the two major scattering components in L-band HH polarization and increased with leaf layer height and density. This paper demonstrated that L-band HH backscatter was more sensitive to rice's structural variation than the VV backscatter and may therefore be more useful in rice mapping and modeling studies. Index Terms--Paddy rice, Phased-Array-Type L-band Synthetic Aperture Radar (PALSAR), radiative transfer model.

Mutated COX-2 has become the new molecular marker of aspirin resistance. However, there is still a technical 'bottleneck' for direct and sensitive detection of circulating COX-2 mutant gene. In this work, we reported a simple and ultrasensitive electrochemical method for COX-2–765G/C (rs20417) detection for the first time. Polyallylamine (PAA) functionalised Pt nanostructures with long-spined sea urchin-like morphology (Pt-LSSUs@PAA) was synthesised by a simple chemical method for the construction of nano-sensing interface. Ru(NH3)63+ is used as a primary electron acceptor that is electrostatically attracted to peptide nucleic acid modified electrodes and Fe(CN)63− is introduced into the redox system as secondary electron acceptor to regenerate Ru3+ after electrochemical reduction for multiple redox cycles. Different pulse voltammetry was applied to record the electrochemical signals. Under optimal conditions, the DNA sensors showed a wide linear relationship, from 10 fM to 1 nM, with detection limits of 3.3 fM (S/N = 3). This study will provide the basis for the precise use of aspirin, and it has important guiding value for individual drug testing of cardiovascular disease. [ABSTRACT FROM AUTHOR]