RESUMO
Low-dimensional tin-based halide perovskites are considered as eco-friendly substitutions of the iconic lead-based perovskites to host the potential as optoelectronic materials. However, a fundamental understanding of the structure-property relationship of these Sn(II)-based hybrids is still inadequate due to the limited members of this material family. To our knowledge, there is still lack of reports on a series of Sn(II)-based halide perovskites with the same organic cation but covering chloride, bromide, and iodide. In this work, three new halide perovskites TMPDASnX4 (X = Cl, Br, I) (TMPDA = N,N,N',N'-tetramethyl-1,4-phenylenediamine) are successfully synthesized, which provide the ideal paradigm to study the halogen-dependent evolution of the structure and properties of Sn(II)-based hybrid perovskites. Despite sharing the same monoclinic lattice (P21/m space group), it is demonstrated that TMPDASnCl4 adopts a one-dimensional structure composed of a five-coordinated pyramid configuration due to an extremely long Sn···Cl distance, while the typical two-dimensional motif is still maintained in TMPDASnBr4 and TMPDASnI4. The ambient stability is declined in the order from chloride to bromide and then to iodide. TMPDASnCl4 exhibits a broad-band bluish-white-light emission (centered at 515 nm, full width at half-maximum (fwhm) = 193 nm) with the Commission Internationale de l' Elairage (CIE) coordinates as (0.29, 0.34). Further, the correlated color temperature and color-rendering index were determined as 7617 K and 80.5, respectively. Based on the synthesis of new crystals, our work sheds light on the composition-structure-property relationship of hybrid Sn(II)-based halide perovskites.
RESUMO
Flood nowcasting refers to near-future prediction of flood status as an extreme weather event unfolds to enhance situational awareness. The objective of this study was to adopt and test a novel structured deep-learning model for urban flood nowcasting by integrating physics-based and human-sensed features. We present a new computational modeling framework including an attention-based spatial-temporal graph convolution network (ASTGCN) model and different streams of data that are collected in real-time, preprocessed, and fed into the model to consider spatial and temporal information and dependencies that improve flood nowcasting. The novelty of the computational modeling framework is threefold: first, the model is capable of considering spatial and temporal dependencies in inundation propagation thanks to the spatial and temporal graph convolutional modules; second, it enables capturing the influence of heterogeneous temporal data streams that can signal flooding status, including physics-based features (e.g., rainfall intensity and water elevation) and human-sensed data (e.g., residents' flood reports and fluctuations of human activity) on flood nowcasting. Third, its attention mechanism enables the model to direct its focus to the most influential features that vary dynamically and influence the flood nowcasting. We show the application of the modeling framework in the context of Harris County, Texas, as the study area and 2017 Hurricane Harvey as the flood event. Three categories of features are used for nowcasting the extent of flood inundation in different census tracts: (i) static features that capture spatial characteristics of various locations and influence their flood status similarity, (ii) physics-based dynamic features that capture changes in hydrodynamic variables, and (iii) heterogeneous human-sensed dynamic features that capture various aspects of residents' activities that can provide information regarding flood status. Results indicate that the ASTGCN model provides superior performance for nowcasting of urban flood inundation at the census-tract level, with precision 0.808 and recall 0.891, which shows the model performs better compared with other state-of-the-art models. Moreover, ASTGCN model performance improves when heterogeneous dynamic features are added into the model that solely relies on physics-based features, which demonstrates the promise of using heterogenous human-sensed data for flood nowcasting. Given the results of the comparisons of the models, the proposed modeling framework has the potential to be more investigated when more data of historical events are available in order to develop a predictive tool to provide community responders with an enhanced prediction of the flood inundation during urban flood.
RESUMO
OBJECTIVE: To assess the value of helical CT in the diagnosis of liver diseases. METHODS: 59 patients with different liver diseases were examined by two-phase or multi-phase dynamic helical CT. RESULTS: Small hepatocellular carcinoma showed a higher density in the arterial phase, and a lower density in the portal vein phase. Large hepatic carcinoma showed a mixed pattern of higher-density in the arterial phase, and a lower density in the portal vein phase. Metastasis carcinoma showed an "oxeye sign" in the portal vein phase. Hemangioma was not obviously enhanced in the early arterial phase, marginally enhanced in the arterial phase, and equally-densed in the balanced phase. CONCLUSION: Two-phase helical CT is of value in improving the detection rate of or determining the features of hepatic diseases by two-phase helical dynamic scan (2.0-3.0 ml/s speed, and delay time 25-30 s and 70-85 s).
