The existing methods have been used the Zenith Total Delay (ZTD) or Precipitable Water Vapor (PWV) derived from Global Navigation Satellite System (GNSS) for rainfall forecasting. However, the occurrence of rainfall is highly related to a myriad of atmospheric parameters, and a good forecast result cannot be obtained if it only depends on a single predictor. This study focused on rainfall forecasting by using a number of atmospheric parameters (such as: temperature, relative humidity, dew temperature, pressure, and PWV) based on the improved Back Propagation Neural Network (BP-NN) algorithm. Results of correlation analysis showed that each meteorological parameter contributed to rainfall. Therefore, a short-term rainfall forecast model was proposed based on an improved BP-NN algorithm by using multiple meteorological parameters. Two GNSS stations and collocated weather stations in Singapore were used to validate the proposed rainfall forecast model by using three years of data (2010-2012). True forecast (TFR), false forecast (FFR), and missed forecast (MFR) rate were introduced as evaluation indices. The experimental result revealed that the proposed model exhibited good performance with TFR larger than 96% and FFR of approximately 40%. The proposed method improved TFR by approximately 10%, whereas FFR was comparable to existing literature. This forecasted result further verified the reliability and practicability of the proposed rainfall forecasting method by using the improved BP-NN algorithm.

GPS-based Zenith Tropospheric Delay (ZTD) estimation should be easily obtained in a cost-effective way, however, the most previous studies focus on post-processed ZTD estimates using satellite orbit and clock products with at least 3-9 hours latency provided by International GNSS Service (IGS), which limits the GNSS meteorological application for nowcasting. With the development of IGS's real-time pilot project (RTPP), this limitation was removed by April, 2013 as real-time satellite orbit and clock products can be obtained on-line. In this paper, on the one hand, the GPS-derived ZTD estimation was evaluated using the IGS final and real-time satellite products based on independently developed PPP software. On the other hand, the analysis of the time series of GPS-derived ZTD by least-square fitting of a broken line tendency for a full year of observations, and a forecasting method for precipitation is proposed based on the ZTD slope in the ascending period. The agreement between ZTD slope and the ground rainfall records suggested that the proposed method is useful for the assisted forecasting, especially for short-term alarms.

Radioresistance is the major cause of cancer treatment failure. Additionally, splicing dysregulation plays critical roles in tumorigenesis. However, the involvement of alternative splicing in resistance of cancer cells to radiotherapy remains elusive. We sought to investigate the key role of the splicing factor SRSF1 in the radioresistance in lung cancer.