No abstract

As a fundamental, holistic, and strategic issue facing human society, human migration is a key factor affecting the development of countries and cities, given the constantly changing population numbers. The fuzziness of the spatiotemporal attributes of human migration limits the pool of open-source data for human migration prediction, leading to a relative lag in human migration prediction algorithm research. This study expands the definition of human migration research, reviews the progress of research into human migration prediction, and classifies and compares human migration algorithms based on open-source data. It also explores the critical uncertainty factors restricting the development of human migration prediction. Based on the analysis, there is no “best” migration prediction model, and data are key to forecasting human migration. Social media’s popularity and its increase in data have enabled the application of artificial intelligence in population migration prediction, which has attracted the attention of researchers and government administrators. Future research will aim to incorporate uncertainty into the predictive analysis framework, and explore the characteristics of population migration behaviors and their interactions. The integration of machine-learning and traditional data-driven models will provide a breakthrough for this purpose.

Comprehensive knowledge of migration involves many fields, such as demographic, economic, social, and political. However, the results of migration theme knowledge retrieval and query are often the knowledge of a single domain, lacking of direct association between different fields. A spatiotemporal migration knowledge graph framework based on migration datasets and Baidu Encyclopedia is presented, and the validity and accuracy of the model are verified by an experiment implemented on the data of 2000. Visualization and Neo4j graph database technologies are used to migration knowledge graph framework which is perfectly adequate for both human beings and machine processing such as graph mining and knowledge reasoning. The migration knowledge graph can quickly and accurately obtain the comprehensive knowledge of migration, significantly improve the ability of human being to apply and analyze the knowledge and data of migration, which has wide theoretical and practical value in the field of migration.

In this work, a general method is presented for the design of arbitrarily shaped 3D illusion devices with piecewise homogeneous parameters based on geometric divisions and linear coordinate transformations. Three illusion devices that can reshape the sizes or positions of the wrapped objects are demonstrated, namely, shrinking, amplifying, and shifting devices. The shrinking device can shrink a larger object into a smaller one with different material parameters, whereas the amplifying device can enlarge a smaller object into a larger one, and a shifting device can generate a new image with an identical size but located at a different position. In addition, based on the presented shrinking device, a perfect 3D invisibility cloak is achieved by shrinking the wrapped object to sufficiently small dimensions as compared to the operating frequency. An electromagnetic concentrator is also obtained by replacing the coated object of the amplifying device with a compression medium. The presented design approach can be easily extended to the design of other electromagnetic devices and even to other physical fields. It is believed that the presented piecewise homogeneous devices are more practicable in reality and can accelerate the potential applications of illusion devices in both military and commercial fields.