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We undertook an autopsy of the drivers of individual foreign real estate investment ‘bust’ in Australia through a new theoretical lens of ‘habitus’. Our autopsy data drew contours around the individual foreign real estate capital ‘boom and bust’ cycle, as well as the long-term commitment of professionals in the real estate sector to Australia’s real estate market. More specifically, we showed that the foreign capital ‘boom and bust’ cycle began in earnest in about 2010 (starting at A$8.7 billion), grew to A$72.4 billion in 2016–2017, and then declined to A$12.5 billion in 2017–2018. This decline in foreign capital into Australian real estate occurred within a domestic real estate market in Sydney that also started to slow in 2017. Based on 20 semi-structured interviews with real estate professionals in Sydney and public material culture data, we found out that the off-the-plan apartment sales and global policy landscape changes contributed to the decline of foreign real estate investment in Australia. The three possible implications for Sydney’s future residential real estate development: (1) The loss of investors, (2) the evolution of the labor force, and (3) the diversification of housing products, have been raised as part of a future research road map.

Numerous studies have been undertaken to determine the optimal land use/cover classification algorithm. However, there have not been many studies that have compared and evaluated the performance of maximum likelihood (ML), random forest (RF), support vector machine (SVM), and classification and regression trees (CART) using ASTER imagery, especially in a mining district. Therefore, this study aims to investigate land use/cover (LULC) change over three decades (1990–2020), comparing the performance of the ML, RF, SVM, and CART machine learning algorithms. The Landsat and ASTER data were retrieved using Google Earth Engine (GEE). Traditional ML classification was performed on ArcGIS 10.2 software while RF, SVM, and CART classification were undertaken on GEE. Then, thematic accuracy assessments were conducted for the four algorithms and their performances were compared. The results showed that the largest changes in area occurred in forest cover that decreased from 37.8 to 27.3 km2 during the three decades. The remarkable expansion of gold mining occurred during 2005–2010 with the increases of 1.6%. The mining land rose by 2.9% during the study period whereas agricultural land increased significantly by 10.7% between 1990 and 2020. When comparing the four algorithms, the RF algorithm gives the highest accuracy with an overall accuracy of 95.85% while SVM follows RF with 91.69%. This study proved that RF is the best choice for optimal land use/cover classification, particularly in the mining district.

One major concern about foreign direct investment (FDI) is the potential negative environmental impact due to increased CO2 emissions. However, there is a possibility that FDI mitigates CO2 emissions through green innovation and creates a cleaner environment. In the existing literature, there is no significant empirical evidence on the linkage among FDI, green innovation and CO2 emissions in the context of BRICS countries. Hence, this study aims to analyze the impact of FDI and green innovation on the environmental quality of BRICS economies for 1990–2014. The study employed Augmented Mean Group (AMG) estimators for empirical data analysis. The study’s findings depict that foreign direct investment, energy use, and economic growth have a significant and positive impact on the CO2 emissions of BRICS economies. Moreover, green innovation has a significant inverse impact on CO2 emissions. The results show bidirectional causalities between CO2 emissions and green innovation, trade openness and CO2 emissions, energy use and CO2 emissions, and urbanization and CO2 emissions. Additionally, the findings reveal a one-way causality from CO2 emissions to GDP and CO2 emissions to urbanization. This study offers essential policy recommendations for the environmental sustainability of BRICS countries through green innovation.

This paper proposes a probabilistic hill-climbing algorithm, called PH, for the single-source transportation problem (STP). PH is a tree search algorithm in which each node contains an assignment problem (AP) transformed from the STP being solved. The transformation converts each source’s product units into product lots; a product lot equals multiple product units. The AP aims to find the optimal assignment of product lots to destinations to minimize the total assignment cost. PH uses the Hungarian method to find the optimal solution of the AP in every node, which is a solution of the STP. For the AP of the root node (as the initial current node), the number of each source’s product lots is set to be small enough to guarantee the generation of a feasible solution for the STP. To generate every subsequent level, the current node is branched into multiple child nodes, in which the number of child nodes equals the number of sources in the STP. The AP of each child node is modified from the AP of the current node by adding one more product lot into a specific different source. Consequently, each child node provides a solution that is better than or the same as the current node’s solution; however, some child nodes’ solutions may be infeasible for the STP due to the insufficiency of a source’s capacity. If all of the child nodes cannot find a better feasible solution than the current node’s solution, PH stops its procedure. To diversify the search, PH selects one of the child nodes as the new current node in a probabilistic way, instead of always selecting the best child node. The experiment’s results in this paper reveal the performance of the three variants of PH.

Ulva Lactuca is a fast-growing algae that can be utilized as a bioenergy source. However, the direct utilization of U. lactuca for energy applications still remains challenging due to its high moisture and inorganics content. Therefore, thermochemical processing such as slow pyrolysis to produce valuable added products, namely bio-oil and biochar, is needed. This study aims to conduct a thorough investigation of bio-oil and biochar production from U. lactuca to provide valuable data for its further valorization. A slow pyrolysis of U. lactuca was conducted in a batch-type reactor at a temperature range of 400–600 °C and times of 10–50 min. The results showed that significant compounds obtained in U. lactuca’s bio-oil are carboxylic acids (22.63–35.28%), phenolics (9.73–31.89%), amines/amides (15.33–23.31%), and N-aromatic compounds (14.04–15.68%). The ultimate analysis revealed that biochar’s H/C and O/C atomic ratios were lower than feedstock, confirming that dehydration and decarboxylation reactions occurred throughout the pyrolysis. Additionally, biochar exhibited calorific values in the range of 19.94–21.61 MJ kg−1, which is potential to be used as a solid renewable fuel. The surface morphological analysis by scanning electron microscope (SEM) showed a larger surface area in U. lactuca’s biochar than in the algal feedstock. Overall, this finding provides insight on the valorization of U. lactuca for value-added chemicals, i.e., biofuels and biochar, which can be further utilized for other applications.

The limited availability of high-resolution monitoring systems for the drought phenomena and water dynamics affected by weather anomalies hinders policy decisions in a multitude of ways. This paper introduces the availability of the high-resolution Water Monitoring System (WMS) developed from a mix of sophisticated multi-spectral satellite imageries, analytic and data sciences, and cloud computing, for monitoring the changes in water levels and vegetation water stress at the local scale. The WMS was tested in the Lower Mekong Region (LMR) case basin, Thailand’s Chi River Basin, in the period from January 2021 to April 2021, the dry season. The overall quality of the VHI, VCI, TCI, and NDVI drought simulation results showed a statistically positive Pearson correlation with the reservoir and dam water volume data (ranged between 0.399 and 0.575) but demonstrated a strong negative correlation with the groundwater level data (between −0.355 and −0.504). Further investigation and more detailed analysis of the influence of different physical environmental conditions related to change in groundwater level should be considered to increase scientific knowledge and understanding about the changing nature of the local system from local perspectives with the alternative use of drought indices in data-poor areas. Our result suggests that the WMS can provide quantitative spatiotemporal variations of localized and contextualized surface water changes as a preliminary analysis. The WMS results can offer guidance for finding a better smaller unit management that suits the local conditions, such as water resource management, disaster risk reduction measures (i.e., drought and flood), irrigation practice, land use planning, and crop management. The existing WMS is geared toward the early warning of water and agricultural development, progress on the SDGs, utilization of digital innovation, and improved abilities of decision-makers to monitor and foresee extreme weather events earlier and with high spatial accuracy.

Bone materials are mainly composed of an inorganic constituent called hydroxyapatite (HA). In the current study, mesoporous Zn2+/Ag+ doped hydroxyapatite nanoparticles (Zn-Ag doped HA) with high antibacterial activity were synthesized through ultrasonic coupled sol-gel techniques under calcination temperatures of 600 °C for 4 h and 1100 °C for 1 h. The variance in the molar ratio of Zn2+/Ag+ in Ca9.0Zn1.0−xAgx(PO4)6(OH)2 (x = 0.0, 0.25 to 1.0) and its effects on the chemical and physical properties of the powdered samples were investigated. The results show that the hexagonal framework of HA incorporated both the Zn2+ and Ag+ ions and the rhombohedral structure of β-TCP. The main functional groups of HA and Zn-Ag doped HA samples were hydroxyl and phosphate. All samples have mesoporous characteristics with a Type IV isotherm. The agar well diffusion process was used to examine antibacterial activity against E. coli, P. aeruginosa, S. aureus, B. cereus and B. subtilis. Effective antibacterial activity was displayed by Zn-Ag doped HA. Excellent antibacterial performance was shown by Ca9.0Zn0.75Ag0.25(PO4)6(OH)2 against all tested bacterial strains, except P. aeruginosa. This material showed inhibition zones ranging from 7 to 11 mm, implying that it is a suitable material with an antibacterial action for environmental applications, specifically for water purification.