A Survey on Centrality Metrics and Their Implications in Network Resilience

Centrality metrics have been used in various networks, such as communication, social, biological, geographic, or contact networks. In particular, they have been used in order to study and analyze targeted attack behaviors and investigated their effect on network resilience. Although a rich volume of centrality metrics has been developed for decades, a limited set of centrality metrics have been commonly in use. This paper aims to introduce various existing centrality metrics and discuss their applicabilities and performance based on the results obtained from extensive simulation experiments to encourage their use in solving various computing and engineering problems in networks.Comment: Main paper: 36 pages, 2 figures. Appendix 23 pages,45 figure

Early warning analysis of mountain flood disaster based on Copula function risk combination

Mountain torrent disaster prevention is the focus of flood control and disaster reduction in China. Critical rainfall is an important indicator to determine the success or failure of mountain torrent disaster early warning. In this paper, the M-Copula function is introduced, the multi-dimensional joint distribution of critical rainfall is constructed, and the joint distribution of rainfall and peak rainfall intensity is analyzed. Taking A village in Xinxian County as an example. The critical rainfall of the combined probability is calculated, and the critical rainfall of the flash flood disaster water level, the pre-shift warning and the sharp-shift warning is warned and analyzed. The results show that the flood peak modulus calculated by Yishangfan group is 8.89, which has certain rules for the flood peak modulus of rivers in hilly areas. The larger the basin area is, the smaller the flood peak modulus is, the smaller the area is, and the larger the flood peak modulus is. The calculation result of the design flow of 533 m3/s is reasonable. It is reasonable and reliable to select the M-Copula function as the connection function to fit the joint distribution of rainfall and peak rainfall intensity, which can provide theoretical support for flash flood disaster warning in other regions

Multistability Analysis, Coexisting Multiple Attractors, and FPGA Implementation of Yu–Wang Four-Wing Chaotic System

In this paper, we further study the dynamic characteristics of the Yu–Wang chaotic system obtained by Yu and Wang in 2012. The system can show a four-wing chaotic attractor in any direction, including all 3D spaces and 2D planes. For this reason, our interest is focused on multistability generation and chaotic FPGA implementation. The stability analysis, bifurcation diagram, basin of attraction, and Lyapunov exponent spectrum are given as the methods to analyze the dynamic behavior of this system. The analyses show that each system parameter has different coexistence phenomena including coexisting chaotic, coexisting stable node, and coexisting limit cycle. Some remarkable features of the system are that it can generate transient one-wing chaos, transient two-wing chaos, and offset boosting. These phenomena have not been found in previous studies of the Yu–Wang chaotic system, so they are worth sharing. Then, the RK4 algorithm of the Verilog 32-bit floating-point standard format is used to realize the autonomous multistable 4D Yu–Wang chaotic system on FPGA, so that it can be applied in embedded engineering based on chaos. Experiments show that the maximum operating frequency of the Yu–Wang chaotic oscillator designed based on FPGA is 161.212 MHz

Design of aluminum nitride metalens for broadband ultraviolet incidence routing

Ultraviolet (UV) photonics-based device and equipment have various applications in sterilization, military covert communication, medical treatment, nanofabrication, gem identification and so on. The traditional constituent UV components are bulky, inefficient, expensive and easily aging under UV radiation. An all-dielectric metasurface offers a promising way to control the amplitude, polarization and phase of light by engineering the size, shape and distribution of its constituent elements. However, UV components based on all-dielectric metasurfaces are difficult to be realized, due to significant absorption loss for most dielectric materials at the UV region. Here we demonstrate the design of a UV metalens, composed of high-aspect-ratio aluminum nitride nanorods. The in-plane on-axis, off-axis and out-of-plane focusing characteristics have been investigated at representative UVA (375 nm), UVB (308 nm) and UVC (244 nm) wavelengths, respectively. Furthermore, we design UV router for mono-wavelength and multiple wavelengths, that is, guiding UV light to designated different spatial positions. Our work is promising for the development of UV photonic devices and would facilitate the integration and miniaturization of the UV nanophotonics

Contribution of the Polarity of Mussel-Inspired Adhesives in the Realization of Strong Underwater Bonding

Although the role of 3,4-dihydroxyphenyl-<i>L</i>-alanine­(DOPA)­in mussel foot proteins (mfps) in the realization of underwater bonding has been widely recognized, the role of the polarity of the polymer was largely overlooked. Here, by systematically comparing the underwater bonding properties of four mussel-inspired adhesives with different amide/lactam contents but similar catechol contents and molecular weights, we came to the conclusion that the polarity of the polymers also contributes to the strong underwater bonding. With the increase in the amide/lactam contents, the polarity of the polymeric adhesive increases, which correlates to the improved underwater bonding strength. A dielectric constant is introduced to evaluate the polarity of the polymer, which may be used as a guidance for the design of mussel-inspired adhesives with even better underwater bonding properties

A Survey on Uncertainty Reasoning and Quantification for Decision Making: Belief Theory Meets Deep Learning

An in-depth understanding of uncertainty is the first step to making effective decisions under uncertainty. Deep/machine learning (ML/DL) has been hugely leveraged to solve complex problems involved with processing high-dimensional data. However, reasoning and quantifying different types of uncertainties to achieve effective decision-making have been much less explored in ML/DL than in other Artificial Intelligence (AI) domains. In particular, belief/evidence theories have been studied in KRR since the 1960s to reason and measure uncertainties to enhance decision-making effectiveness. We found that only a few studies have leveraged the mature uncertainty research in belief/evidence theories in ML/DL to tackle complex problems under different types of uncertainty. In this survey paper, we discuss several popular belief theories and their core ideas dealing with uncertainty causes and types and quantifying them, along with the discussions of their applicability in ML/DL. In addition, we discuss three main approaches that leverage belief theories in Deep Neural Networks (DNNs), including Evidential DNNs, Fuzzy DNNs, and Rough DNNs, in terms of their uncertainty causes, types, and quantification methods along with their applicability in diverse problem domains. Based on our in-depth survey, we discuss insights, lessons learned, limitations of the current state-of-the-art bridging belief theories and ML/DL, and finally, future research directions.Comment: First four authors contributed equall