Construction of Hydrogen Safety Evaluation Model Based on Analytic Hierarchy Process (AHP)

With the large consumption of traditional primary energy, hydrogen as a clean and renewable energy has been widely studied by scholars around the world. Hydrogen is mainly used in hydrogen internal combustion engine and hydrogen fuel cell. Hydrogen internal combustion engine is the direct combustion of hydrogen as fuel, with the advantages of easy use. Alternatively, hydrogen fuel cell converts the chemical energy of hydrogen into electrical energy by electrochemical reaction, which has the advantages of high efficiency and zero pollution. Regardless of the use method, the safety of hydrogen use needs to be considered. However, in the whole life cycle of hydrogen, the process from hydrogen production to the use of hydrogen in automobiles is extremely complex. There are many factors affecting the safety of hydrogen use, and a single factor cannot be used as an evaluation. In order to make the evaluation of hydrogen safety more complete and accurate, the weight of four primary evaluation indexes and eight secondary evaluation indexes affecting hydrogen safety is determined by analytic hierarchy process, and a reliable hydrogen safety evaluation model is established.Citation: Xu, J., Wang, M., and Guo, P. (2022). Construction of Hydrogen Safety Evaluation Model Based on Analytic Hierarchy Process (AHP). Trends in Renewable Energy, 8(2), 84-95. DOI: http://dx.doi.org/10.17737/tre.2022.8.2.0014

Fujita Critical Curve for a Coupled Diffusion System with Inhomogeneous Neumann Boundary Conditions∗

In this paper, we establish the blow-up theorems of Fujita type for a class of exterior problems of nonlinear diffusion equations subject to inhomogeneous Neumann boundary conditions. The critical Fujita exponents are determined and it is shown that the critical curve belongs to the blow-up case under any nontrivial initial data

When NAS Meets Robustness: In Search of Robust Architectures against Adversarial Attacks

Recent advances in adversarial attacks uncover the intrinsic vulnerability of modern deep neural networks. Since then, extensive efforts have been devoted to enhancing the robustness of deep networks via specialized learning algorithms and loss functions. In this work, we take an architectural perspective and investigate the patterns of network architectures that are resilient to adversarial attacks. To obtain the large number of networks needed for this study, we adopt one-shot neural architecture search, training a large network for once and then finetuning the sub-networks sampled therefrom. The sampled architectures together with the accuracies they achieve provide a rich basis for our study. Our "robust architecture Odyssey" reveals several valuable observations: 1) densely connected patterns result in improved robustness; 2) under computational budget, adding convolution operations to direct connection edge is effective; 3) flow of solution procedure (FSP) matrix is a good indicator of network robustness. Based on these observations, we discover a family of robust architectures (RobNets). On various datasets, including CIFAR, SVHN, Tiny-ImageNet, and ImageNet, RobNets exhibit superior robustness performance to other widely used architectures. Notably, RobNets substantially improve the robust accuracy (~5% absolute gains) under both white-box and black-box attacks, even with fewer parameter numbers. Code is available at https://github.com/gmh14/RobNets.Comment: CVPR 2020. First two authors contributed equall

Geometric Information and Rational Parametrization of Nonsingular Cubic Blending Surfaces

The techniques for parametrizing nonsingular cubic surfaces have shown to be of great interest in recent years. This paper is devoted to the rational parametrization of nonsingular cubic blending surfaces. We claim that these nonsingular cubic blending surfaces can be parametrized using the symbolic computation due to their excellent geometric properties. Especially for the specific forms of these surfaces, we conclude that they must be 3, 4, or 5 surfaces, and a criterion is given for deciding their surface types. Besides, using the algorithm proposed by Berry and Patterson in 2001, we obtain the uniform rational parametric representation of these specific forms. It should be emphasized that our results in this paper are invariant under any nonsingular real projective transform. Two explicit examples are presented at the end of this paper

A New Channel of Bulge Formation via the Destruction of Short Bars

Short (inner) bars of subkiloparsec radius have been hypothesized to be an important mechanism for driving gas inflows to small scales, thus feeding central black holes (BHs). Recent numerical simulations have shown that the growth of central BHs in galaxies can destroy short bars, when the BH reaches a mass of ∼0.1% of the total stellar mass of the galaxy. We study N-body simulations of galaxies with single and double bars to track the long-term evolution of the central stellar mass distribution. We find that the destruction of the short bar contributes significantly to the growth of the bulge. The final bulge mass is roughly equal to the sum of the masses of the initial pseudo bulge and short bar. The initially boxy/peanut-shaped bulge of Sérsic index n1 is transformed into a more massive, compact structure that bears many similarities to a classical bulge, in terms of its morphology (n≈2), kinematics (dispersion-dominated, isotropic), and location on standard scaling relations (Kormendy relation, mass-size relation, and correlations between BH mass and bulge stellar mass and velocity dispersion). Our proposed channel for forming classical bulges relies solely on the destruction of short bars without any reliance on mergers. We suggest that some of the less massive, less compact classical bulges were formed in this manner