Abstract-This paper presents an effective modeling methodology for Ultra-wideband (UWB) antennas. The methodology is based on augmenting an existing narrow-band model with a macro-model while simultaneously perturbing component values of the narrow-band model. The narrow-band model is an empirical-based circuit and the macro-model described by rational functions is determined using data fitting approaches. The perturbation of component values of the narrow-band model is achieved by adjustments in SPICE. This method is demonstrated on the example of a 2.5 cm dipole antenna and a circular disc monopole antenna for UWB systems. Simulation results show that this methodology is effective over a wide bandwidth and suitable for modeling most UWB antennas.

Pancreatic Ductal Adenocarcinoma (PDAC) is a deadly disease that has an increasing death rate but no effective treatment to now. Although biological and immunological hallmarks of PDAC have been frequently reported recently, early detection and the particularly aggressive biological features are the major challenges remaining unclear. In the current study, we retrieved multiple scRNA-seq datasets and illustrated the genetic programs of PDAC development in genetically modified mouse models. Notably, the transcription levels of Id1 were elevated specifically along with the PDAC development. Pseudotime trajectory analysis revealed that Id1 was closely correlated with the malignancy of PDAC. The gene expression patterns of human PDAC cells were determined by the comparative analysis of the scRNA-seq data on human PDAC and normal pancreas tissues. ID1 levels in human PDAC cancer cells were dramatically increased compared to normal epithelial cells. ID1 deficiency in vitro significantly blunt the invasive tumor-formation related phenotypes. IPA analysis on the differentially expressed genes suggested that EIF2 signaling was the core pathway regulating the development of PDAC. Blocking EFI2 signaling remarkably decreased the expression of ID1 and attenuated the tumor-formation related phenotypes. These observations confirmed that ID1 was regulated by EIF2 signaling and was the critical determinator of PDAC development and progression. This study suggests that ID1 is a potential malignant biomarker of PDAC in both mouse models and human and detecting and targeting ID1 may be a promising strategy to treat or even rescue PDAC.

Identifying the land-use type and spatial distribution of urban construction land is the basis of studying the degree of exposure and the economic value of disaster-affected bodies, which are of great significance for disaster risk predictions, emergency disaster reductions, and asset allocations. Based on point of interest (POI) data, this study adopts POI spatialization and the density-based spatial clustering of applications with noise (DBSCAN) algorithm to accomplish the spatial classification of construction land. Zhejiang province is selected as a study area, and its construction land is divided into 11 land types using an accurate spatial classification method based on measuring the area of ground items. In the research, the POI dataset, which includes information, such as spatial locations and usage types, was constructed by big data cleaning and visual interpretation and approximately 620,000 pieces in total. The overall accuracy of the confusion matrix is 76.86%, which is greatly improved compared with that constructed with EULUC data (61.2%). In addition, compared with the official statistical data of 11 cities in Zhejiang Province, the differences between the calculated spatial proportions and statistics are not substantial. Meanwhile, the spatial characteristics of the studied land-use types are consistent with the urban planning data but with higher accuracy. The research shows that the construction land in Zhejiang Province has a high degree of land intensity, concentrated assets, and high economic exposure. The approach proposed in this study can provide a reference for city management including urbanization process, risk assessment, emergency management and asset allocation.

Near-surface typhoon wind field simulation is a key method for high-precision typhoon hazard assessment and is of great significance for disaster forecast, loss risk assessment and emergency management. The terrain correction method for regional large-scale wind field simulation has a single correction method, which cannot satisfy the requirements of refined risk assessment. This paper aims to use the accuracy advantage of the fluid dynamics mechanism model (CFD, computational fluid dynamics) in small-scale wind speed simulation and obtain a terrain correction method suitable for regional largescale wind field simulation by extracting the spatial change law of the wind speed under complex terrain.Among them, typical mountains with different cross-sectional shapes and different slopes are used to characterize the undulating terrain, and the CFD model is used to simulate and analyze the wind speed changes on the upwind slope, mountain top area, leeward slope, and downwind area under different initial wind speeds. The wind speed at these locations has a good quantitative relationship with the initial wind speed. Combined with the common building wind load codes in China, the wind speed correction algorithm suitable for large-scale complex terrain has been supplemented and improved. Taking Typhoon 0713 as an example, a near-surface typhoon wind field simulation was performed. Compared with other models, the accuracy of the simulation results of terrain correction by the method provided in this article has increased by 15-19.9%. The CFD-based typhoon disaster near-surface wind field can more accurately reflect the spatial distribution and intensity of typhoon wind hazards under large-scale complex terrain. It can provide technical support for the loss risk prediction and assessment of forest resources, mountain forestry economy, crops and other disaster-bearing bodies in typhoon disasters.