Measuring air connectivity between China and Australia

This paper assesses air connectivity between China and Australia for the period 2005–16 using a Connectivity Utility Model. Our direct connectivity measure shows that as a gateway city, Sydney continues to play a key role in facilitating the movements of people and goods between China and Australia. Guangzhou has become the city best connected with Australia since 2011 as measured by direct connectivity. When indirect connections are considered, the largest increases in overall connectivity from 2005 to 2016 can be observed among Australia's major capital cities, particularly Sydney, Melbourne and Brisbane. Chinese carriers are the key drivers behind the increases. There have been rises and falls for airports serving as a hub between China and Australia. Guangzhou has forged its strong status as a transfer hub between Australia and China thanks to the quick expansion of China Southern. The gaps between Guangzhou and other transfer hubs measured by hub connectivity have widened since 2010

Genome-Wide Analysis of Sorghum GT47 Family Reveals Functional Divergences of MUR3-Like Genes

Sorghum (Sorghum bicolor) is an important bioenergy crop. Its biomass mainly consists of the cellulosic and non-cellulosic polysaccharides, both which can be converted to biofuels. The biosynthesis of non-cellulosic polysaccharides involves several glycosyltransferases (GT) families including GT47. However, there was no systemic study on GT47 family in sorghum to date. Here, we identified 39 sorghum GT47 family members and showed the functional divergences of MURUS3 (MUR3) homologs. Sorghum GT47 proteins were phylogenetically clustered into four distinct subfamilies. Within each subfamily, gene structure was relatively conserved between the members. Ten gene pairs were identified from the 39 GT47 genes, of which two pairs might be originated from tandem duplication. 25.6% (10/39) of sorghum GT47 genes were homologous to Arabidopsis MUR3, a xyloglucan biosynthesis gene in primary cell walls. SbGT47_2, SbGT47_7, and SbGT47_8, three most homologous genes of MUR3, exhibited different tissue expression patterns and were selected for complementation into Arabidopsis mur3-3. Physiological and cell wall analyses showed that SbGT47_2 and SbGT47_7 may be two functional xyloglucan galactosyltransferases in sorghum. Further studies found that MUR3-like genes are widely present in the seed plants but not in the chlorophytic alga Chlamydomonas reinhardtii. Our results provide novel information for evolutionary analysis and functional dissection of sorghum GT47 family members

Genome-Wide Identification and Expression Profiling Analysis of the Xyloglucan Endotransglucosylase/Hydrolase Gene Family in Tobacco (Nicotiana tabacum L.)

Xyloglucan endotransglucosylase/hydrolase genes (XTHs) encode enzymes required for the reconstruction and modification of xyloglucan backbones, which will result in changes of cell wall extensibility during growth. A total of 56 NtXTH genes were identified from common tobacco, and 50 cDNA fragments were verified by PCR amplification. The 56 NtXTH genes could be classified into two subfamilies: Group I/II and Group III according to their phylogenetic relationships. The gene structure, chromosomal localization, conserved protein domains prediction, sub-cellular localization of NtXTH proteins and evolutionary relationships among Nicotiana tabacum, Nicotiana sylvestrisis, Nicotiana tomentosiformis, Arabidopsis, and rice were also analyzed. The NtXTHs expression profiles analyzed by the TobEA database and qRT-PCR revealed that NtXTHs display different expression patterns in different tissues. Notably, the expression patterns of 12 NtXTHs responding to environment stresses, including salinity, alkali, heat, chilling, and plant hormones, including IAA and brassinolide, were characterized. All the results would be useful for the function study of NtXTHs during different growth cycles and stresses

The growing influence of low-cost carriers in Northeast Asia and its implications for a regional single aviation market

This paper provides an overview of the development of the low-cost carrier (LCC) sector in China, Japan, and South Korea. It is the first paper that documents LCC contributions to the passenger traffic and cheaper fares in Northeast Asia (NEA)’s intra-markets. We argue that a single aviation market can facilitate the growth of the LCC sector, which in turn will make a significant contribution to the NEA connectivity, mobility, and integration. In addition, with a single aviation market, NEA countries can adopt a proactive, unified approach in negotiating air transport agreements with the major aviation partners to maximize the interests of this region as a whole, which will further provide valuable growth opportunities for the LCCs

Parameter Identification and Linear Model of Giant Magnetostrictive Vibrator

A linear magnetization model is built to replace the Jiles–Atherton model in order to describe the relationship between the magnetic field intensity and the magnetization intensity of the giant magnetostrictive vibrator (GMV). The systematic modeling of the GMV is composed of three aspects, i.e., the structural mechanic model, the magnetostrictive model, and the Jiles–Atherton model. The Jiles–Atherton model has five parameters to be defined; hence, its solution is so complex that it is not convenient in application. Therefore, the immune genetic algorithm (IGA) is applied in the identification of the five parameters of the Jiles–Atherton model and it showed a higher stability compared with the identification result of the differential evolution algorithm (DEA). The identification parameters of the two algorithms were employed, respectively, to calculate the excitation force and it was found that the relative error of IGA was evidently smaller than that of DEA, indicating that the former was more reliable than the latter. According to the identification results of IGA and based on the least square method (LSM), curve-fittings to the magnetic field intensity and magnetization intensity were conducted by using the linear function. And the linear magnetization model was built to replace the Jiles–Atherton model. Research results show that the linear model of the GMV can be established by combining the linear magnetization model with the structural mechanic model as well as the giant magnetostrictive model. The linear magnetization model, which has great engineering application value, can be applied in the open-loop control of the vibrator

COVID-19, air transportation, and international trade in the ASEAN+5 region

This paper provides an in-depth description of the coronavirus disease (COVID-19) pandemic and its interactions with air transportation in the Association of Southeast Asian Nations (ASEAN) + 5 region, and then links the changes in air connectivity to trade using a gravity regression model. We find that almost all the countries probably reacted too late in their decision to reduce flights in the early stage of the pandemic. As the pandemic evolved, most countries have significantly cut the number of flight connections, especially international flights. The reduced connectivity is found to have a significantly negative impact on trade for time-sensitive merchandise that is essential to consumers and businesses. This points to the importance of the region seeking alternative arrangements to restore air connectivity. We offer a way to construct optimal travel bubbles by using the introduced risk indexes. Other policy issues such as uniform standards and regulations, and regional ‘open skies’, are also discussed

Snowmelt Flood Susceptibility Assessment in Kunlun Mountains Based on the Swin Transformer Deep Learning Method

Modeling and assessing the susceptibility of snowmelt floods is critical for flood hazard management. However, the current research on snowmelt flood susceptibility lacks a valid large-scale modeling approach. In this study, a novel high-performance deep learning model called Swin Transformer was used to assess snowmelt susceptibility in the Kunlun Mountains region, where snowmelt floods occur frequently. Support vector machine (SVM), random forest (RF), deep neural network (DNN) and convolutional neural network (CNN) were also involved in the performance comparison. Eighteen potential conditioning factors were combined with a historical flood inventory to form the database. Apart from the susceptibility assessment, sensitivity analysis was also conducted to reflect the impact of the conditioning factors on the susceptibility of different types of snowmelt floods. The results showed that Swin Transformer achieved the highest score in the model performance test (AUC = 0.99) and successfully identified the relationship between conditioning factors and snowmelt flooding. Elevation and distance to rivers are the most important factors that affect snowmelt flooding in the study region, whereas rainfall and snow water equivalent are the dominant natural factors for mixed and warming types. In addition, the north-central parts of the study area have high susceptibility to snowmelt flooding. The methods and results can provide scientific support for snowmelt flood modeling and disaster management

Integration of UAV and GF-2 Optical Data for Estimating Aboveground Biomass in Spruce Plantations in Qinghai, China

More refined and economical aboveground biomass (AGB) monitoring techniques are needed because of the growing significance of spruce plantations in climate change mitigation programs. Due to the challenges of conducting field surveys, such as the potential inaccessibility and high cost, this study proposes a convenient and efficient alternative to traditional field surveys that integrates Gaofen-2 (GF-2) satellite optical images and unmanned aerial vehicle (UAV)-acquired optical and point cloud data to provide a reliable and refined estimation of the aboveground biomass (AGB) in spruce plantations. The feasibility of using data produced from the semiautomatic processing of UAV-based images and photogrammetric point clouds to replace conventional field surveys of sample plots in a young spruce plantation was evaluated. The AGB in 53 sample plots was estimated using data extracted from the UAV imagery. The UAV plot data and GF-2 optical data were used in four regression models to estimate the AGB in the study area. The coefficient of determination (R2), root-mean-square error (RMSE), mean percent standard error (MPSE), and Lin’s concordance correlation coefficient (LCCC) were calculated through five-fold cross-validation and stratified random sampling to evaluate the models’ efficacies. In the end, the most accurate model was used to generate the spatial distribution map of the AGB. The results revealed the following: (1) the individual-tree height (R2 = 0.90) and crown diameter (R2 = 0.74) extracted from UAV data were accurate enough to replace field surveys used to obtain the AGB at the plot levels; (2) the random forest (RF) model (R2 = 0.86; RMSE = 1.75 t/ha; MPSE = 15.75%; LCCC = 0.91) outperformed the ordinary least-squares (OLS) model (R2 = 0.68; RMSE = 2.49 t/ha; MPSE = 22.94%; LCCC = 0.81), artificial neural network (ANN) model (R2 = 0.67; RMSE = 2.54 t/ha; MPSE = 21.48%; LCCC = 0.80), and support vector machine (SVM) model (R2 = 0.60; RMSE = 2.84 t/ha; MPSE = 31.73%; LCCC = 0.76) in terms of the estimation accuracy; (3) an AGB map generated by the random forest model was in good agreement with field surveys and the age of the spruce plantations. Therefore, the method proposed in this study can be used as a refined and cost-effective way to estimate the AGB in young spruce plantations