Around 80% of global trade by volume is transported by sea, and thus the maritime transportation system is fundamental to the world economy. To better exploit new international shipping routes, we need to understand the current ones and their complex systems association with international trade. We investigate the structure of the global liner shipping network (GLSN), finding it is an economic small-world network with a trade-off between high transportation efficiency and low wiring cost. To enhance understanding of this trade-off, we examine the modular segregation of the GLSN; we study provincial-, connector-hub ports and propose the definition of gateway-hub ports, using three respective structural measures. The gateway-hub structural-core organization seems a salient property of the GLSN, which proves importantly associated to network integration and function in realizing the cargo transportation of international trade. This finding offers new insights into the GLSN’s structural organization complexity and its relevance to international trade.

Maritime shipping is a backbone of international trade and, thus, the world economy. Cargo-loaded vessels travel from one country's port to another via an underlying port-to-port transport network, contributing to international trade values of countries en route. We hypothesize that ports that involve trans-shipment activities serve as a third-party broker to mediate trade between two foreign countries and contribute to the corresponding country's status in international trade. We test this hypothesis using a port-level dataset of global liner shipping services. We propose two indices that quantify the importance of countries in the global liner shipping network and show that they explain a large amount of variation in individual countries' international trade values and related measures. These results support a long-standing view in maritime economics, which has yet to be directly tested, that countries that are strongly integrated into the global maritime transportation network have enhanced access to global markets and trade opportunities.

This study is proposed to reconstruct a high-resolution spatial distribution of historical land use pattern with all land use types to overcome low-accuracy and/or the monotonic land use type in current historical land use reconstruction studies. The year of 1820 is set as the temporal section and the administrative area of Jiangsu Province is the study area. Land use types being reconstructed include farmland, residential land (including both urban land and rural residential land), water body, and other land (including forest land, grassland, and unused land). Data sources mainly refer to historical documents, historical geographic research outcomes, contemporary statistics, and natural environmental data. With great considerations over regional natural resources and social and economic conditions, a few theoretical assumptions have been proposed to facilitate the adjustment on prefecture farmland, urban land, and rural residential land. Upholding the idea that the contemporary land use pattern has been inherently in sequence with the historical land use pattern as well as the land use pattern shall be consistent to its accessibility, this study reconstructs the land use pattern in Jiangsu Province in 1820 with 100 m*100 m grids based on accessibility analysis and comprehensive evaluation. The outcome has been tested as valid by regionalization and correlation analysis. The resulted spatial distribution shows that back in 1820 in Jiangsu Province: (1) farmland, urban land, rural residential land, water body, and other land take about 48.49%, 4.46%, 0.16%, 15.03%, and 31.86% of the total land area respectively; (2) the land use pattern features high proportion of land in farming while low-proportion land in non-farming uses while population, topography, and the density of water body lead to great spatial variations; and (3) the reconstruction methodology has been tested as reasonable based on significant positive correlations between 1820 data and 1985 for both farmland and rural residential land at the prefecture level.

Fractal and self similarity of complex networks have attracted much attention in recent years. The fractal dimension is a useful method to describe the fractal property of networks. However, the fractal features of mobile social networks (MSNs) are inadequately investigated. In this work, a box-covering method based on the ratio of excluded mass to closeness centrality is presented to investigate the fractal feature of MSNs. Using this method, we find that some MSNs are fractal at different time intervals. Our simulation results indicate that the proposed method is available for analyzing the fractal property of MSNs.

Yellow rust is a disease with a wide range that causes great damage to wheat. The traditional method of manually identifying wheat yellow rust is very inefficient. To improve this situation, this study proposed a deep-learning-based method for identifying wheat yellow rust from unmanned aerial vehicle (UAV) images. The method was based on the pyramid scene parsing network (PSPNet) semantic segmentation model to classify healthy wheat, yellow rust wheat, and bare soil in small-scale UAV images, and to investigate the spatial generalization of the model. In addition, it was proposed to use the high-accuracy classification results of traditional algorithms as weak samples for wheat yellow rust identification. The recognition accuracy of the PSPNet model in this study reached 98%. On this basis, this study used the trained semantic segmentation model to recognize another wheat field. The results showed that the method had certain generalization ability, and its accuracy reached 98%. In addition, the high-accuracy classification result of a support vector machine was used as a weak label by weak supervision, which better solved the labeling problem of large-size images, and the final recognition accuracy reached 94%. Therefore, the present study method facilitated timely control measures to reduce economic losses.