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Up-to-date digital elevation model (DEM) products are essential in many fields such as hazards mitigation and urban management. Airborne and low-earth-orbit (LEO) space-borne interferometric synthetic aperture radar (InSAR) has been proven to be a valuable tool for DEM generation. However, given the limitations of cost and satellite repeat cycles, it is difficult to generate or update DEMs very frequently (e.g., on a daily basis) for a very large area (e.g., continental scale or greater). Geosynchronous synthetic aperture radar (GEOSAR) satellites fly in geostationary earth orbits, allowing them to observe the same ground area with a very short revisit time (daily or shorter). This offers great potential for the daily DEM generation that is desirable yet thus far impossible with space-borne sensors. In this work, we systematically analyze the quality of daily GEOSAR DEM. The results indicate that the accuracy of a daily GEOSAR DEM is generally much lower than what can be achieved with typical LEO synthetic aperture radar (SAR) sensors; therefore, it is important to develop techniques to mitigate the effects of errors in GEOSAR DEM generation.


China's past economic growth has substantially relied on fossil fuels, causing serious air pollution issues. Decoupling economic growth and pollution has become the focus in developing ecological civilization in China. We have analyzed the three-decade progress of air pollution controls in China, highlighting a strategic transformation from emission control toward air quality management. Emission control of sulfur dioxide (SO2) resolved the deteriorating acid rain issue in China in 2007. Since 2013, control actions on multiple precursors and sectors have targeted the reduction of the concentration of fine particulate matter (PM2.5), marking a transition to an air-quality-oriented strategy. Increasing ozone (O3) pollution further requires O3 and PM2.5 integrated control strategies with an emphasis on their complex photochemical interactions. Fundamental improvement of air quality in China, as a key indicator for the success of ecological civilization construction, demands the deep de-carbonization of China's energy system as well as more synergistic pathways to address air pollution and global climate change simultaneously.


微震/声发射震源定位方法可以对结构的潜在危险源进行预测与控制。然而,在现有的定位方法中,由不规则结构和预测速度引起的定位误差却没有得到很好地解决。为了实现复杂三维含孔结构的高精度定位要求,本文提出了一种三维含孔洞结构的无需测速震源定位方法。该算法采用等距网格点搜索路径,避免了人工重复训练;引入了A*搜索算法,并利用网格点来适应具有不规则孔洞的复杂结构;利用了无需预先测速的定位方法的优点。为了验证新方法的有效性,在尺寸为10 cm×10 cm×10 cm的立方体混凝土构件上,掏出一个尺寸为ϕ6 cm×10 cm的圆柱形空区,并在该构件上进行断铅试验。根据到达时间,我们分别用经典的Geiger法和新方法进行定位计算。结果显示,新方法的定位误差为1.20 cm,远小于Geiger法的2.02 cm。这表明新方法可以在含孔洞的复杂三维结构中进行有效定位,并能达到较高的精度要求。


可再生能源(RES)被认为是可靠的绿色发电能源。光伏(PV)和风机(WT)被用来为偏远地区提供电力。在独立环境中,确定混合型RES的最佳容量是一个非常重要的挑战。过去提出的元启发式算法依赖于特定算法的参数来获得最优解。本文提出了一种基于Jaya的混合算法和一种基于教学的优化(TLBO),即JLBO算法,用于确定光伏-风机-电池混合系统的最优单位容量,从而以最小的年度总成本(TAC)满足消费者的负载。系统的可靠性由最大允许的负荷缺电率(LPSP max)来衡量。将JLBO算法的结果与原Jaya、TLBO和遗传算法进行比较。JLBO算法的计算结果在TAC方面显示了优越性能,并且确定光伏-风机-电池混合系统是最经济的方案。与光伏-电池和风机-电池系统相比,该系统为所有提议的最大负荷缺电率都提供了一个经济高效的解决方案。


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绿色化学工程
高性能结构:建筑结构与材料
免疫
食品安全与健康