Financing Sources, R&D Investment and Enterprise Risk

AbstractResearch and development (R&D) investment of high-tech enterprises has an impact on enterprise risk, but the effect is different when funding sources are different. This paper aims to study the relationships among financing sources, R&D investment and enterprise risk. The empirical results suggest that the relationship between endogenous financing rate and R&D investment is significantly positive, and asset-liability ratio has a significantly negative impact on R&D investment. Furthermore, the study shows that relationship between enterprise risk and R&D investment can be described with a quadratic parabola

The application of fractal dimension on capillary pressure curve to evaluate the tight sandstone

The rock of gas tight reservoir is more heterogeneous than that of conventional sandstone reservoir and is more prone to water-blockage because of the invasion of operation fluid. This paper presented a new approach for the analysis of the capillary pressure curve for tight gas reservoir. Herein, the saturation equation with fractal dimension proved the previous observation that the log-log plot of capillary pressure against water saturation is a straight line, which is quite different from the popular observation by Corey’s correlation. How to transform the capillary pressure curve to relative permeability curve was also discussed with fractal dimension. The fractal dimension of capillary pressure, which is not only an indication of heterogeneity, can also reveal the potential water blocks in tight gas wells. If the rock has higher fractal dimension, being at the same water saturation, the capillary pressure will be higher and the relative permeability of water will be smaller, which means higher injection pressure is required to displace the trapped water in reservoir. It is suggested that for the tight gas pay zone with higher fractal dimension, more precautions should be taken to prevent the water trapping during drilling or stimulating operation

Road Segmentation for Remote Sensing Images using Adversarial Spatial Pyramid Networks

Road extraction in remote sensing images is of great importance for a wide range of applications. Because of the complex background, and high density, most of the existing methods fail to accurately extract a road network that appears correct and complete. Moreover, they suffer from either insufficient training data or high costs of manual annotation. To address these problems, we introduce a new model to apply structured domain adaption for synthetic image generation and road segmentation. We incorporate a feature pyramid network into generative adversarial networks to minimize the difference between the source and target domains. A generator is learned to produce quality synthetic images, and the discriminator attempts to distinguish them. We also propose a feature pyramid network that improves the performance of the proposed model by extracting effective features from all the layers of the network for describing different scales objects. Indeed, a novel scale-wise architecture is introduced to learn from the multi-level feature maps and improve the semantics of the features. For optimization, the model is trained by a joint reconstruction loss function, which minimizes the difference between the fake images and the real ones. A wide range of experiments on three datasets prove the superior performance of the proposed approach in terms of accuracy and efficiency. In particular, our model achieves state-of-the-art 78.86 IOU on the Massachusetts dataset with 14.89M parameters and 86.78B FLOPs, with 4x fewer FLOPs but higher accuracy (+3.47% IOU) than the top performer among state-of-the-art approaches used in the evaluation