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In order to meet the demands of desirable efficiency, transformerless DC/DC equipment with great voltage step-down are inevitable needed. This research offers a unique type of high-frequency, high-voltage-gain DC/DC converter, which comprises a switched capacitor (SC) converter and a buck converter. Thanks to the transformation of a two-stage converter to a single-stage converter, it has a considerable ratio of step-down voltage transformation and a reasonable duty cycle. In addition, it can permit low voltage stress on the switches. The simple control method and easy driving circuit implementation makes it scalable for high-power-level devices. Low cost can be realized as fewer components are needed. Under all operational circumstances, total soft-charging and low equipment voltage stresses are accomplished. Compared to those classic high-voltage-gain converters, the proposed converter exhibits merits of higher efficiency, higher flexibility, lower ripples, and lower costs. A comprehensive analysis is carried out for the converter's steady-state operations. With a 1 MHz switching frequency, a 900 W prototype of a 20-time converter is constructed, with a peak efficiency of 92.5%. Simulations and experiments verify the effectiveness of the theoretical investigation of the converter's operation. [ABSTRACT FROM AUTHOR]

Accurate and spatially-distributed precipitation information is vital to the study of the regional hydrological cycle and water resources, as well as for environmental management. To provide high spatio-temporal resolution precipitation estimates over insufficient rain-gauge areas, great efforts have been taken in using the Normalized Difference Vegetation Index (NDVI) and other land surface variables to improve the spatial resolution of satellite precipitation datasets. However, the strong spatio-temporal heterogeneity of precipitation and the "hysteresis phenomenon" of the relationship between precipitation and vegetation has limited the application of these downscaling methods to high temporal resolutions. To overcome this limitation, a new temporal downscaling method was proposed in this study by introducing daily soil moisture data to explore the relationship between precipitation and the soil moisture increment index. The performance of this proposed temporal downscaling was assessed by downscaling the Tropical Rainfall Measuring Mission (TRMM) precipitation data from a monthly scale to a daily scale over the Hekouzhen to Tongguan of the Yellow River in 2013, and the downscaled daily precipitation datasets were validated with in-situ measurement from 23 rainfall observation stations. The validation results indicate that the downscaled daily precipitation agrees with the rain gauge observations, with a correlation coefficient of 0.59, a mean error range of 1.70 mm, and a root mean square error of 5.93 mm. In general, the monthly precipitation decomposition method proposed in this paper has combined the advantage of both microwave remote sensing products. It has acceptable precision and can generate precipitation on a diurnal scale. It is an important development in the field of using auxiliary data to perform temporal downscaling. Furthermore, this method also provides a reference example for the temporal downscaling of other low temporal resolution datasets. [ABSTRACT FROM AUTHOR]