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In the real Internet of Everything scenario, many large-scale information systems can be converted into interdependent sensor networks, such as smart grids, smart medical systems, and industrial Internet systems. These complex systems usually have multiple interdependent sensor networks. Small faults or failure behaviors between networks may cause serious cascading failure effects of the entire system. Therefore, in this paper, we will focus on the security of interdependent sensor networks. Firstly, by calculating the size of the largest functional component in the entire network, the impact of random attacks on the security of interdependent sensor networks is analyzed. Secondly, it compares and analyzes the impact of cascading failures between interdependent sensor networks under different switching edge strategies. Finally, the simulation results verify the effect of the security of the system under different strategies, and give a better exchange strategy to enhance the security of the system. In addition, the research work in this article can help design how to further optimize the topology of interdependent sensor networks by reducing the impact of cascading failures.

Strategic users in a service exchange application of crowdsensing are apt to exhibit malicious behaviors such as greed, free-ride, and attack, resulting in the phenomenon that no user is willing to serve others and low social utility is obtained in myopic equilibrium, which is considered as a service exchange contest dilemma. To address this issue, we propose a game-theoretic framework of multi-level two-sided rating protocol using all-pay contests to balance service request and service provision between users, in which a user is tagged with a multi-level rating to represent her social status, and she is encouraged to take the initiative to be a server and provide high-quality services to increase her rating. The two-sided rating update rule updates the ratings of both service requesters and service providers, and thus no one can always get services without providing services. By quantifying necessary and sufficient conditions for a sustainable multi-level two-sided rating protocol, we formulate the problem of selecting the optimal design parameters to maximize the social utility among all sustainable multi-level two-sided rating protocols, and design a low-complexity algorithm to select optimal design parameters via a two-stage procedure in an alternate manner. Finally, the extensive evaluation results demonstrate how intrinsic parameters impact on recommended strategies, design parameters, as well as the performance gain of the proposed rating protocol.

Locating sources in a large network is of paramount importance to reduce the spreading of disruptive behavior. Based on the backward diffusion-based method and integer programming, we propose an efficient approach to locate sources in complex networks with limited observers. The results on model networks and empirical networks demonstrate that, for a certain fraction of observers, the accuracy of our method for source localization will improve as the increase of network size. Besides, compared with the previous method (the maximum–minimum method), the performance of our method is much better with a small fraction of observers, especially in heterogeneous networks. Furthermore, our method is more robust against noise environments and strategies of choosing observers.

In this paper, we study the robustness of the Cyber-Physical System (CPS), which consists of interdependent physical resources and computational resources. Numerous infrastructure systems can evolve into the Cyber-Physical System. These networks depend on their interdependent networks, which provide information or energy to function. In a CPS architecture, a small failure could trigger serious cascading failures within the entire interdependent networks. Different systems’ structures would influence the performance of swapping inter links strategies on improving the reliability of networks. We observe that reducing the influence of cascading failures is independent from the attack strategies and can be optimized by adjusting a Cyber-Physical System topology.

Locating the source node that initiates a diffusion process is an increasingly popular topic that contributes new insights into the maintenance of cyber security, rumor detection in social media, digital surveillance of infectious diseases, etc. Existing studies select the observers randomly or select them heuristically according to the network centrality or community measures. However, there still lacks a method to identify the minimum set of observers for accurately locating the source node of information diffusion in cyber physical networks. Here, we fill this knowledge gap by proposing a greedy optimization algorithm by analyzing the differences of the propagation delay. We use extensive simulations with both synthetic and empirical networks to show that the number of observers can be substantially decreased: Our method only uses a small fraction of nodes (10%–20%) as observers in most networks, whereas the conventional random selection methods have to use 2–3 times more nodes as observers. Interestingly, if a network has a large proportion of low-degree nodes (e.g., karate network), it is necessary to recruit more observers. In particular, the periphery nodes that are only connected with one edge must be observers. Combining our greedy optimization algorithm with the diffusion-back method, the performance of source localization is robust against noise.

The finite element models of Cosserat continuum in two- and three-dimensions are presented. The size effects of a cantilever beam and a micro-rod, the well-posedness, the mesh-independent solutions of the boundary value problems with non-associated elastoplastic and strain softening constitutive behavior, and the progressive failure of the two- and three-dimensional vertical excavations are studied. Numerical results illustrate that the proposed Cosserat continuum models are capable of reflecting the size effects of micro-structures, preserving the well-posedness of the boundary value problem characterized by the strain localization, ensuring mesh-independent solutions, and simulating the entire progressive failure process occurring in engineering structures.

Due to the multi-scale fusion of cyber-physical systems, attackers can attack the physical space based on cyber space intentionally. This process can cause cascading failures and then in sharp contrast with the previous physical space. Thus, how to effectively evaluate the security of cyber-physical systems becomes critical. In this paper, we model the cyber-physical systems and then analyze the cascading failure process under target attack strategy. After doing that, based on the comparative analysis of simulation experiments, we analyze the main factors affecting the security of the cyber-physical system.

In this paper, the reliability performance analysis of coupled cyber-physical systems under different network types is investigated. To study the underlying network model, we propose a practical model for interdependent cyber-physical systems using network percolation theory. For different network models, we also study the effect of cascading failures effect and reveal mathematical analysis of failure propagation in such systems. The simulation results show that there exists a threshold for the proportion of faulty nodes and different system parameters, beyond which the cyber-physical systems collapse.

Check-in data play an increasingly important role in mining users’ behavioral patterns and personalized recommendation etc. However, check-in data abuse also rises users’ concern about their privacy. Obfuscating one’s check-in data to another’s is an effective method. Existing approaches focus more on privacy preservation than on data utility. To the end, this paper proposes a two-step choice method. The method first clusters users into several clusters based on users’ check-in data. Then the method chooses a cluster, named substituting cluster, which is the nearest to the cluster which the publishing user belongs to. The cluster which the publishing user belongs to is named original cluster. The substituting cluster and the original cluster should satisfy the predefined privacy threshold. The second step is to find a user in substituting cluster, named substituting user, whose check-in data are the most similar to the publishing user. Finally, the method replaces the publishing user check-in data by the substituting user’s. Empirical evaluation shows that the proposed method can not only preserve the user’s privacy effectively, but also preserve higher data utility.

The fact that most of cyber security problems are interdependent security problems, in which one agent’s security is affected not only by this agent’s security investment but also by the security decisions of his neighbors, has encouraged more and more people to study security games and security investment on large computer networks. In this paper, we study interdependent security problems by modeling the problems as public goods games on structured populations. We put forward an incentive mechanism named quantized externality mechanism to improve the overall social payoff in evolutionary public goods games of security investment on complex networks. Moreover, we introduce local exit equilibrium into our new mechanism to satisfy voluntary participation principle. Simulation results show that our mechanism can play an important role in increasing the level of security investment on complex networks. We also find that the participation rate of our new mechanism can converge to a stable level.

Networks play a role that interactions through which behaviors and diseases can spread. Identifying or locating all the sources in a large network is an important step towards understanding the transmission mechanism. Based on the network structure and backward diffusion-based method, we propose a maximum-largest method to locate sources with limited observers. Results of applying this method to modeling networks and empirical networks demonstrate that our method is superior on a larger networks size for a certain fraction of observers. Besides, our method is very robust for different strategies of choosing observers. Furthermore, the performance of our method is better than the previous method (the maximum–minimum method), especially for a small fraction of available observers. What is more, the performance of our method can be further improved by virtue of Gaussian kernel, which is very robust against noise case. Our analysis provides a route for improving source localization in large networks.

The effects of topically administered snake (Deinagkistrodon acutus) oil and its main fatty acid components on skin photodamage were explored. Epidermal thickness, mice body weight, antioxidant enzymes (superoxide dismutase, glutathione peroxidase, catalase), inflammatory cytokines (tumor necrosis factor alpha and interleukin-6), skin histology, collagen content, and metalloproteinase-1 indicators were analyzed. The results show that topical application of both snake oil and its main fatty acids recovered ultraviolet B (UVB) irradiation induced antioxidant enzymes depletion, prevented metalloproteinase-1. Snake oil and its main fatty acids suppressed dermal infiltration of inflammatory cells and reduced inflammatory cytokines levels. Notably, there was no significant difference in the antioxidant activity but a significant difference in the anti-inflammatory activity between fatty acids and snake oil under the same dose. Finally, snake oil and its main fatty acids inhibited UVB-induced histological damage such as epidermal thickening, collagen fiber and elastic fiber destruction. Our study demonstrated for the first time in KM mice that snake oil exhibited prominent photoprotection activity by protecting the activity of antioxidant enzymes and inhibiting inflammatory factors, as well as reducing the generation of MMP-1. What's more, the main fatty acids in snake oil play an important role in preventing photo-damage especially in protecting antioxidant enzyme activity.

A plethora of polymer-based scaffolds have been designed to facilitate biochemical and biophysical investigation of membrane proteins, with a common goal to stabilize and present them in a functional format. In this review, an up-to-date account of such polymer-based supports and incorporation methodologies are presented. Furthermore, conceptual and imminent technological advances, with associated technical challenges are proposed.

The coupling of proteins with self-assembly properties and proteins that are capable of recognizing and mineralizing specific inorganic species is a promising strategy for the synthesis of nanoscale materials with controllable morphology and functionality. Herein, GPG-AG3 protein fibers with both of these properties were constructed and served as templates for the synthesis of Pt and Pd nanotubes. The protein fibers of assembled GPG-AG3 were more than 10 μm long and had diameters of 20-50 nm. The as-synthesized Pt and Pd nanotubes were composed of dense layers of ~3-5 nm Pt and Pd nanoparticles. When tested as cathodes in lithium-O2 batteries, the porous Pt nanotubes showed low charge potentials of 3.8 V, with round-trip efficiencies of about 65% at a current density of 100 mA g(-1).

A transversely isotropic Cosserat continuum model is developed based on the Cosserat continuum theory and transversely isotropic elastic theory in three-dimensional condition. The finite element program based on this model is developed to analysis the stress and deformation of a transversely isotropic layered soils subjected to a loading on an elastic rectangular plate. It illustrates that the transversely isotropic model can reflect the soil’s property more realistically than the isotropic one in some practical engineering, and that the proposed transversely isotropic Cosserat continuum model can describe micro-scale mechanical property of soils and simulate the mechanical behavior of the transversely isotropic soils effectively.