http://scholars.ntou.edu.tw/handle/123456789/199 2026-04-24T16:13:13Z http://scholars.ntou.edu.tw/handle/123456789/26505 標題: Assessing CO₂ sources and sinks in and around Taiwan: Implication for achieving regional carbon neutrality by 2050( vol 206 , 116664, 2024) 作者: Hung, Chin-Chang; Hsieh, Hsueh-Han; Chou, Wen-Chen; Liu, En-Chi; Chow, Chun Hoe; Chang, Yi; Lee, Tse-Min; Santsch, Peter Hans; Ranatunga, R. R. M. K. P.; Bacosa, Hernando P.; Shih, Yung-Yen 2025-01-01T00:00:00Z http://scholars.ntou.edu.tw/handle/123456789/26456 標題: Spatiotemporal deep learning fusion of radar, infrared, and geospatial data for typhoon rainfall estimation in Taiwan 作者: Wei, Chih-Chiang; Chiu, Chih-Chia 摘要: Taiwan, located in the Northwest Pacific typhoon corridor, faces frequent tropical cyclones that trigger extreme rainfall and heighten flood risks, underscoring the urgency of accurate short-term forecasts. This study presents a deep learning framework-the Recurrent Spatiotemporal Fusion Module (RSFM)-which integrates rainfall accumulation, radar reflectivity, infrared satellite imagery, and geospatial data to enhance typhoon-induced rainfall prediction. Trained on 36 typhoon events from 2013 to 2023, RSFM employs semantic segmentation combined with recurrent encoding to effectively capture spatiotemporal precipitation patterns. Among three input scenarios, the full multi-source configuration reduced RMSE by up to 9%, achieving values between 3.9 and 6.6 mm/h over 1-6 h forecasts. It also improved regional accuracy, with Bias Ratios approaching 1.0 and ETS exceeding 0.55 in terrain-affected southern and eastern Taiwan. Compared to traditional radar QPE, satellite-based PERSIANN-CCS, and ConvLSTM models, RSFM demonstrated superior skill in localizing convective cores and adapting to diverse typhoon structures, as confirmed through case studies of Typhoons Soulik, Trami, Soudelor, and Megi. These results highlight RSFM's promise as a robust tool for operational early warning systems and hydrological planning in typhoon-prone, mountainous regions, offering valuable support for disaster preparedness under a changing climate. 2025-01-01T00:00:00Z http://scholars.ntou.edu.tw/handle/123456789/26431 標題: Ocean Observations 作者: Ho, Chung-Ru 2025-01-01T00:00:00Z http://scholars.ntou.edu.tw/handle/123456789/26414 標題: Tuning Ternary Deep Red Exciplex-Forming Hosts to Achieve a Stable OLED with EL Peak Centered at 834 nm 作者: Chen, Yi-Yun; Kung, Yu-Cheng; Tsai, Cheng-Han; Wang, Chun-Kai; Luo, Dian; Chen, Yi-Sheng; Liu, Shun-Wei; Hsu, Allen Chu-Hsiang; Hung, Wen-Yi; Wong, Ken-Tsung 摘要: This study explores new ternary exciplex-forming systems comprising a deep red-emitting CPF:58p-QN blend and various ratios of spacer TPF to optimize donor-acceptor interactions and exciplex characteristics. Time-resolved photoluminescence reveals delayed fluorescence of CPF:58p-QN:TPF blends, confirming the thermally activated delayed fluorescence (TADF) characters. By introducing different ratios of TPF, a progressive blueshift emission wavelength ranging from 696 nm (without TPF) to 659 nm (50 wt.% TPF) is observed. Notably, device A2, featuring CPF:58p-QN:TPF (2:2:1) blend as emitting layer, achieves a maximum external quantum efficiency (EQE(max)) of 2.13% with the electroluminescent peak (EL lambda(max)) centered at 672 nm. Moreover, a fluorescence emitter iCzPBBT is introduced as a dopant to realize a near-infrared (NIR) emissive device. Device B2, utilizing the CPF:58p-QN:TPF (2:2:1) blend as host doped with 5 wt.% iCzPBBT, exhibits an EQE(max) of 1.35% (EL lambda(max) = 848 nm), demonstrating effective energy transfer from exciplex to NIR dopant. Device C2 with a reduced amount of iCzPBBT (2 wt.%) to mitigate concentration quenching achieves an EQE(max) of 1.72% (EL lambda(max) = 834 nm) and good stability (LT90 > 88 h under a constant current density of 0.6 mA cm(-)(2)). This study underscores the potential of a ternary exciplex-forming system as a promising host for NIR OLED applications. 2025-01-01T00:00:00Z