While the method for estimating the Palmer Drought Severity Index (PDSI) is now more closely aligned to key water balance components, a comprehensive assessment for measuring long-term droughts that recognizes meteorological, agro-ecological and hydrological perspectives and their attributions is still lacking. Based on physical approaches linked to potential evapotranspiration (PET), the PDSI in 1965–2014 showed a mixture of drying (42% of the land area) and wetting (58% of the land area) that combined to give a slightly wetting trend (0.0036 per year). Despite the smaller overall trend, there is a switch to a drying trend over the past decade (−0.023 per year). We designed numerical experiments and found that PDSI trend responding to the dramatic increase in air temperature and slight change in precipitation. The variabilities of meteorological and agro-ecological droughts were broadly comparable to various PDSI drought index. Interestingly, the hydrological drought was not completely comparable to the PDSI, which indicates that runoff in arid and semi-arid regions was not generated primarily from precipitation. Instead, fraction of glacierized areas in catchments caused large variations in the observed runoff changes.

In this perspective, we outline that a space borne gravitational wave detector network combining LISA and Taiji can be used to measure the Hubble constant with an uncertainty less than 0.5% in ten years, compared with the network of the ground based gravitational wave detectors which can measure the Hubble constant within a 2% uncertainty in the next five years by the standard siren method. Taiji is a Chinese space borne gravitational wave detection mission planned for launch in the early 2030 s. The pilot satellite mission Taiji-1 has been launched in August 2019 to verify the feasibility of Taiji. The results of a few technologies tested on Taiji-1 are presented in this paper.

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As a cost saving and profit-making strategy, a modular design is being employed in developing complex products and systems (CoPS) in recent decades. At the early stage of design, the reliability of a product can be improved by identifying the influential function modules based on the modular function architecture. In this study, the weighted LeaderRank algorithm and susceptible-infected-recovered (SIR) model of weighted and directed complex networks (WDCNs) are employed to identify the influential function modules of modular CoPS at the conceptual design stage. First, the structure of the function module is obtained and is mapped into a WDCN. Second, based on the similarity between the behaviors of nodes in the WDCN and function modules in the CoPS, a node-identification approach based on the weighted LeaderRank algorithm is employed to identify the influential function modules, whose influences are then verified through the SIR model. The influential function modules of a modular large tonnage crawler crane are determined as a case study to demonstrate the effectiveness and validity of the developed method.