SF Networks Research Articles

The evolution of dual-phase core–shell structure and mechanical properties induced by Co addition in as-cast high-entropy intermetallic

In this work, three as-cast L12-type HEIs with different content of Co element were carefully examined combined with TEM, SEM and XRD technologies. The main matrix of three alloys was L12 ordered phase, and Si,Ti-rich grain-boundary precipitate phase generated with the addition of Co. In Co1 alloy, distinct dual-phase core–shell structure was found within grain due to the precipitation of extra FCC disordered secondary phase, which leads to a better strength-plasticity combination (compressive strength of 3441 ± 50 MPa, and fracture strain of 46.0 ± 1.5%) than Co0 and Co0.5 alloy at room temperature. With the increase of temperature, particular anomalous yield effect was discovered in Co1 alloy. It is clear that SFs became main deformation substructure in Co1 alloy at elevated temperature, and the interaction between non-coplanar SFs motivated the generation of L-C Locks, which assists the K-W Locks to pin dislocations movement and enhance the yield strength from room temperature to about 650 ℃. In addition, the interaction between SFs resulted in the formation of nano-spaced SF network, which contributes to the dynamic Hall-Petch effect. Finally, excellent high-temperature yield strength was achieved in as-cast Co1 alloy, which is higher than room-temperature yield strength until 950 ℃.

Impact of contact rate on epidemic spreading in complex networks.

Contact reduction is an effective strategy to mitigate the spreading of epidemic. However, the existing reaction-diffusion equations for infectious disease are unable to characterize this effect. Thus, we here propose an extended susceptible-infected-recovered model by incorporating contact rate into the standard SIR model, and concentrate on investigating its impact on epidemic transmission. We analytically derive the epidemic thresholds on homogeneous and heterogeneous networks, respectively. The effects of contact rate on spreading speed, scale and outbreak threshold are explored on ER and SF networks. Simulations results show that epidemic dissemination is significantly mitigated when contact rate is reduced. Importantly, epidemic spreads faster on heterogeneous networks while broader on homogeneous networks, and the outbreak thresholds of the former are smaller.

Reconfiguring dual-phase structure with equal micro-hardness to achieve double strength with slight ductility sacrifice

• After flash annealing, the hardness of ferrite and austenite is equal. • The steel obtains the doubling yield strength with slight elongation sacrifice. • Hall-Petch strengthening contributes 60% to yield strength, 40% by HDI hardening. • The high strain hardening rate originates from the multiple deformation mechanisms. • The multilevel SF networks and high-density L-C locks can improve strain hardening. The application of duplex stainless steel is severely restricted owing to its inherently low yield strength. A high yield strength of duplex stainless steel is required to address the lightweight issues and reduce the emission of polluting gases. Herein, excellent mechanical properties of duplex stainless steel were achieved using a flash annealing treatment to obtain austenite and ferrite phases with equal hardness, and the yield strength doubled without a substantial ductility sacrifice. The improvement in yield strength was caused by the grain boundary strengthening (a contribution of 60% to the yield strength) and hetero-deformation-induced strengthening (remaining 40% contribution to the yield strength) owing to the significant difference in the strain partitioning between the two phases. The high strain-hardening capability ensured ductility without a significant decrease; this correlated to the multiple microstructure deformation mechanisms. These mechanisms included the synergetic deformation of the ferrite with dislocation cell formation and austenite accompanied by the sequential appearance of dislocation accumulation, stacking faults, and deformation-induced nanoscale twins and martensite. Deformation-induced multilevel stacking fault networks and high-density Lomer-Cotterll locks further improved the strain hardening response by enhancing the hetero-deformation behavior of austenite and ferrite.

Security Situation Awareness Assessment of Heterogeneous Cyber-Physical Systems in Multiple Load Mode

As the Internet of Things technology develops rapidly, cyber-physical systems (CPSs) have provided critical technologies in the industrial field. A smart grid is a typical application of the CPS, which is formed by the coupling of the power network and communication network. In general, the two networks are heterogeneous. Considering the characteristics of the power network, the ordinary percolation network model is not suitable for the network carrying physical flow. Therefore, we propose an interdependent network model with a load. That is, nodes in the physical network have loaded. Then, we research the security of CPS under the multi-load mode. The rule for node failure is different from the percolation model. When a node’s load exceeds its capacity, the node fails. Through the theoretical analysis, we obtained the iterative equation of the percolation threshold and analyzed it through simulation experiments. We adopt both linear and nonlinear capacity models. It is found that appropriately increasing the average degree of ER network can improve its ability to resist attacks. In contrast, the power exponent of the SF network has less influence on the critical value of network percolation. In addition, we found that increasing the capacity parameter in the linear capacity model can improve the robustness of the network, where the variation of the ER-ER network is more evident than that of the SF-SF network. Increasing the capacity parameter in the nonlinear capacity model can significantly increase the network’s ability to resist attacks. However, due to the increase in economical cost, we can improve the network’s reliability by increasing the node capacity while controlling the cost.

Link cooperation effect of cooperative epidemics on complex networks

Epidemic spreading dynamic is usually used to describe the virus spread in the biology system and information diffusion in the social system. Previous studies indicated that multiple epidemics rarely evolve dependently, but constantly interact and coevolve. In this paper, a novel mathematical model is proposed to study the link cooperation effect of two epidemics cooperatively spreading on complex networks. The link cooperation effect means that the first epidemic is transmitted through this link, and the second epidemic will have a higher transmission probability when it is transmitted through the same link. We develop an edge-based compartmental method and can well predict the numerical simulations. We find the link cooperation effect promote the epidemic outbreak size. The phase transition is closely relates to the strength of the link cooperation effect and network topology. For the cooperate epidemics on ER networks, the phase transition is discontinuous for a stronger link cooperation effect, otherwise the phase transition is continuous. When cooperative epidemics spread on SF networks, the phase transition is always continuous because of the existence of a few hubs. Our developed theoretical method can reasonably predict the above phenomena that are obtained from the numerical simulations based on the proposed mathematical model.

Cascading-Failures Effect on Heterogeneous Internet of Things Systems under Targeted Selective Attack

With the rapid development of the Internet of Things (IoT), the physical system and the network space are further deeply integrated, forming a larger-scale IoT heterogeneous fusion system. The attack mode considered in the security mechanism research of traditional large-scale complex systems is relatively simple; only simple attack types such as random attacks on physical systems or network systems are considered. In addition, existing attack modalities such as selectivity, locality, and distribution cannot fully consider the characteristics of security threats in the IoT system. In this paper, for large-scale heterogeneous IoT system scenarios, attackers can attack network systems or physical systems through cyberspace. We conduct situational awareness analysis on important traffic nodes or backbone nodes and study the cascading failures of two interdependent heterogeneous space systems. In view of the existence of such targeted attack threats in large-scale IoT heterogeneous systems, we focus on security assessment and risk prediction issues. First, this paper analyzes and models different IoT heterogeneous systems. Then using the penetration theory, we analyze the cascading failure process step by step and obtain the critical threshold for system collapse failure. Finally, we further verify the correctness of the theoretical values through simulation to effectively analyze and illustrate the reliability of the parameters affecting the system risk. The experimental results show that the large-scale IoT heterogeneous system presents a first-order discontinuous transition value near the critical threshold and the power-law index of the SF network has little effect on the system security.

Enhanced strength-ductility synergy via novel bifunctional nano-precipitates in a high-entropy alloy

High-entropy alloys (HEAs) with a single-phased face-centered-cubic structure possess excellent plasticity but generally low strength. Precipitation strengthening is one of the most promising methods to improve the strength of alloys. However, plagued by a nerve-wracking fact that strength-ductility trade-off frequently limits the improvement of alloy properties. To overcome this problem, a new Ni35(CoFe)55V5Nb5 HEA with an excellent strength and ductility synergy was developed by introducing a novel bifunctional L12-Ni3Nb nano-precipitate. This HEA exhibits a high yield strength of 855 MPa, ultimate tensile strength of 1,302 MPa and marvelous elongation of ∼ 50%. First-principles calculations show that the (Ni24Co18Fe6)3(Nb10V4Fe2) nano-precipitate with a L12 structure possesses lower formation energy than that with D022 structure. The novel nano-precipitates provide two-fold functions. On the one hand, L12-(Ni24Co18Fe6)3(Nb10V4Fe2) nano-precipitates have a high anti-phase boundary energy, contributing to a significant increment in the yield strength through precipitation strengthening. More importantly, the precipitation of the precipitates lowers the stacking fault energy (SFE) of the alloy matrix, contributing to the excellent work-hardening ability and large plasticity through activating the continuous formation of SF networks and Lomer-Cottrell locks during deformation. The strategy to introduce the novel bifunctional nano-precipitates paves a new way to enhance the strength-ductility synergy of alloys.

Robustness of scale-free networks with dynamical behavior against multi-node perturbation

An issue which is increasingly attracting attention from scientists to engineers, is the robustness of networks which is the ability against perturbations. It is found that both the network topology and network dynamics affect the robustness of networks. In this article, we present the cascading failure model triggered by perturbing a fraction 1−p of nodes on SF networks with three dynamics: the biochemical(B), epidemic(E) and regulatory(R) dynamics. A mathematical method is developed to calculate the cascading failure size and the giant component to evaluate the robustness when a fraction 1−p of nodes is perturbed on SF dynamical networks. We perform extensive numerical simulations to test and verify this formula and find that the theoretical results are in good agreement with simulations. The results show that the network is more robust as the tolerance coefficient δ increases, and the size of network has little influence on the robustness, especially for B and R. Remarkably, the heterogeneity of networks is positive on the robustness. Moreover, the different characteristics that as the parameter B increases or the parameter R decreases the network with B is more robust, and as the parameter R increases or the parameter B decreases the network with E and R is more robust are found. These findings may be useful for engineers to improve the robustness of the network or design robust networks with dynamics.

Self-adhesive, biodegradable silk-based dry electrodes for epidermal electrophysiological monitoring

Epidermal flexible devices can continuously and real-time record the electrical potential changes from human skin, which is essential for chronic disease diagnosis and smart health monitoring. However, it remains challenging to fabricate high-performance epidermal dry electrodes with excellent self-adhesive while maintaining excellent biodegradability as well as high mechanical performance and low contact impedance. Herein, we fabricate an epidermal dry electrode with high mechanical performance, low contact impedance and excellent self-adhesive by coating polypyrrole (Ppy) conductive layer and calcium (Ca)-modified SF adhesive layer on acid-modified silk (AM-SF)/cellulose nanocrystals (CNC) films. The AM-SF could effectively promote the absorption and intercalation of Ppy into the SF network, endowing the tight integration between the Ppy conductive layer and the AM-SF/CNC films and improving the conductivity of Ppy@AM-SF/CNC films. On the other side, the Ca ions in the Ca-modified SF adhesive layer capture water molecules from the atmosphere and result in a much higher flowability to allow conformal contact between Ca-modified SF and the human skin, leading to significantly low skin-electrode impedance. As a proof-of-concept, the Ppy@AM-SF/CNC electrodes are employed for both electrocardiography (ECG) and electromyography (EMG) recording. Interestingly, this electrode allows detaching from the skin by simple water rinsing to reduce skin-damage for people. Moreover, the Ppy@AM-SF/CNC electrodes possess excellent enzymatic degradability without leaving hazardous substances. This work offers a unique prospective for developing next-generation wearable healthcare devices in the era of personalized healthcare.

Boost of the Bio-memristor Performance for Artificial Electronic Synapses by Surface Reconstruction.

Biomaterial-based memristors (bio-memristors) are often adopted to emulate biological synapse functions and applied to construct neural computing networks in brain-inspired chip systems. However, the randomness of conductive filament formation in bio-memristors inhibits their switching performance by causing the dispersion of the device-switching parameters. In this case, a facile porous silk fibroin (p-SF) memristor was obtained through a protein surface reconstruction strategy, in which the size of the hole can be adjusted by the density of hybrid nanoseeds. The porous SF memristors exhibit greatly enhanced electrical characteristics, including uniform I-V cycles, centralized distribution of the switching voltages, and both high and low resistances, compared to devices without pores. The results of three-dimensional (3D) simulations based on classical density functional theory (cDFT) suggest that the reconstructed pores in the SF layers guide the formation and fracture of Ag filaments under an electric field and enhance the overall conductivity by separating Ag+ ion and electron diffusion pathways. Ag+ ions are predicted to preferentially diffuse through pores, whereas electrons diffuse through the SF network. Interestingly, the device conductance can be bidirectionally modulated gradually by positive and negative voltages, can faithfully simulate short-term and long-term plasticity, and can even realize the triplet-spike-timing-dependent plasticity (triplet-STDP) rule, which can be used for pattern recognition in biological systems. The simulation results reveal that a memristor network of this type has an accuracy of ∼95.78% in memory learning and the capability of pattern learning. This work provides a facile technology route to improve the performance of bionic-material memristors.

Modelling cascading failures in networks with the harmonic closeness

Many studies on cascading failures adopt the degree or the betweenness of a node to define its load. From a novel perspective, we propose an approach to obtain initial loads considering the harmonic closeness and the impact of neighboring nodes. Based on simulation results for different adjustable parameter θ, local parameter δ and proportion of attacked nodes f, it is found that in scale-free networks (SF networks), small-world networks (SW networks) and Erdos-Renyi networks (ER networks), there exists a negative correlation between optimal θ and δ. By the removal of the low load node, cascading failures are more likely to occur in some cases. In addition, we find a valuable result that our method yields better performance compared with other methods in SF networks with an arbitrary f, SW and ER networks with large f. Moreover, the method concerning the harmonic closeness makes these three model networks more robust for different average degrees. Finally, we perform the simulations on twenty real networks, whose results verify that our method is also effective to distribute the initial load in different real networks.

Cooperate epidemic spreading on multiplex networks with heterogeneous population

Cooperate epidemic spreading dynamics has attracted much attention from the field of network science. In this paper, we study the cooperate epidemic spreading dynamics on multiplex networks with heterogeneous populations, which induces the heterogeneous coinfection susceptibility. We propose a spreading model to describe the evolution mechanisms. To predict the final state of the epidemic outbreak size, a generalized bond percolation theory is suggested. Through numerical simulations and theoretical analyses, we find that the system exhibits a discontinuous phase transition for large average and small variance of the distribution of coinfection susceptibility on ER–ER multiplex networks, while the phase transition is continuous on SF–SF networks. In addition, the final outbreak size increases with the average coinfection susceptibility and decreases with the variance of the coinfection susceptibility. Our suggested bond percolation theory can well predict the numerical simulations.

Restoration of interdependent network against cascading overload failure

Many networks are physically or logically interdependent with each other, such as smart power grid, city traffic network and communication systems, where cascading overload failure becomes a major threat. Based on a load-dependent cascading model, we investigate the restoration characteristics in the consideration of repair resource, timing and load tolerance, for different coupling strength and network topologies in interdependent networks. We find that the restoration on the network with different coupling strength may lead to two extreme system effects with early repair: full recovery or completely collapse. Furthermore, SF–SF network is sensitive to repair resources, while repair effect of ER–ER network increases sharply when load tolerance is increased. When overloads are triggered in an ER network coupled with a SF network, the restoration effect can be obviously worse than other topology combinations. Our findings may help to design restoration strategy for interdependent networks and improve the system resilience.

Social contagions with heterogeneous credibility

An individual’s credibility is strongly affected by their social status. Assuming that a person’s level of credibility correlates with their degree from a microscopic perspective, we propose a non-Markovian model to understand how the heterogeneity of credibility levels affects social contagions within a population. To describe the model, we develop a heterogeneous edge-based compartmental theory. Through extensive numerical simulations, we find that the growth pattern of the final adoption size versus the information transmission probability is discontinuous, and that in ER networks the final adoption size increases when hubs have high levels of credibility. When hubs in an ER network have low levels of credibility, the growth pattern is continuous. We also study social contagions on SF networks and find that the growth pattern versus information transmission probability is always continuous. On both ER and SF networks, the final adoption size versus the heterogeneity parameter exhibits a discontinuous pattern. The results of our theory agree well with those of numerical simulations.

Information spreading in complex networks with participation of independent spreaders

Information diffusion dynamics in complex networks is often modeled as a contagion process among neighbors which is analogous to epidemic diffusion. The attention of previous literature is mainly focused on epidemic diffusion within one network, which, however neglects the possible interactions between nodes beyond the underlying network. The disease can be transmitted to other nodes by other means without following the links in the focal network. Here we account for this phenomenon by introducing the independent spreaders in a susceptible–infectious–recovered contagion process. We derive the critical epidemic thresholds on Erdős–Rényi and scale-free networks as a function of infectious rate, recovery rate and the activeness of independent spreaders. We also present simulation results on ER and SF networks, as well as on a real-world email network. The result shows that the extent to which a disease can infect might be more far-reaching, than we can explain in terms of link contagion only. Besides, these results also help to explain how activeness of independent spreaders can affect the diffusion process, which can be used to explore many other dynamical processes.

The independent spreaders involved SIR Rumor model in complex networks

Recent studies of rumor or information diffusion process in complex networks show that in contrast to traditional comprehension, individuals who participate in rumor spreading within one network do not always get the rumor from their neighbors. They can obtain the rumor from different sources like online social networks and then publish it on their personal sites. In our paper, we discuss this phenomenon in complex networks by adopting the concept of independent spreaders. Rather than getting the rumor from neighbors, independent spreaders learn it from other channels. We further develop the classic “ignorant-spreaders-stiflers” or SIR model of rumor diffusion process in complex networks. A steady-state analysis is conducted to investigate the final spectrum of the rumor spreading under various spreading rate, stifling rate, density of independent spreaders and average degree of the network. Results show that independent spreaders effectively enhance the rumor diffusion process, by delivering the rumor to regions far away from the current rumor infected regions. And though the rumor spreading process in SF networks is faster than that in ER networks, the final size of rumor spreading in ER networks is larger than that in SF networks.

Compression-induced stacking fault tetrahedra around He bubbles in Al

Classic molecular dynamics methods are used to simulate the uniform compression process of the fcc Al containing He bubbles. The formation of stacking fault tetrahedra (SFTs) during the collapse of He bubbles is found, and their dependence on the initial He bubble size (0.6–6 nm in diameter) is presented. Our simulations indicate only elastic deformation in the samples for the He bubble size not more than 2 nm. Instead, increasing the He bubble size, we detect several small SFTs forming on the surface of the He bubble (3 nm), as well as the two intercrossed SFTs around the He bubbles (4–6 nm). All these SFTs are observed to be stable under further compression, though there may appear some SF networks outside the SFTs (5–6 nm). Furthermore, the dynamic analysis on the SFTs shows that the yield pressure keeps a near-linear increase with the initial He bubble pressure, and the potential energy of Al atoms inside the SFTs is lower than outside because of their gliding inwards. In addition, the pressure increments of 2–6 nm He bubbles with strain are less than that of Al, which just provides the opportunity for the He bubble collapse and the SFTs formation. Note that the current work only focuses on the case that the number ratio between He atoms and Al vacancies is 1:1.

Dynamics of the Kuramoto Model with Bimodal Frequency Distribution on Complex Networks

We introduce a piecewise uniform frequency distribution to model a symmetrical bimodal natural frequency distribution and investigate the dynamics in the Kuramoto model on complex networks. We find that the scenario of the synchronization transition depends on the network topology. For an ER network, the incoherent state, standing wave states and stationary synchronous states are encountered successively with the increase of the coupling strength. However, for an SF network, there exists another type of synchronous states, traveling wave states, between the standing wave states and the stationary synchronous states.

The effect of zealots on the rate of consensus achievement in complex networks

In this study, we investigate the role of zealots on the result of voting process on both scale-free and Watts–Strogatz networks. We observe that inflexible individuals are very effective in consensus achievement and also in the rate of ordering process in complex networks. Zealots make the magnetization of the system to vary exponentially with time. We obtain that on SF networks, increasing the zealots’ population, Z, exponentially increases the rate of consensus achievement. The time needed for the system to reach a desired magnetization, shows a power-law dependence on Z. As well, we obtain that the decay time of the order parameter shows a power-law dependence on Z. We also investigate the role of zealots’ degree on the rate of ordering process and finally, we analyze the effect of network’s randomness on the efficiency of zealots. Moving from a regular to a random network, the re-wiring probability Prw increases. We show that with increasing Prw, the efficiency of zealots for reducing the consensus achievement time increases. The rate of consensus is compared with the rate of ordering for different re-wiring probabilities of WS networks.

A navigation search model based on subnet of maximum controllability

In this paper, we propose a concept of subnet of maximum controllability based on the model of network controllability, and set up the navigation search model based on the subnet of maximum controllability, called NMSMC. The strategy of adding links that is based on the subnet of maximum controllability is to solve, with the minimum cost, the terminating search, the problem that arises from no way for particles to search in the directed network. Based on the subnet of maximum controllability to deploy navigation nodes,the search time of the whole network can be made close to the average shortest path of the navigation network,which the number of navigation nodes is only 2% of the total nodes. The experimental results of the ER and SF networks show that the search efficiency is strongly correlated with the network controllability. The better the controllability, the less the adding links are, which can lead to the fact that the more the navigation nodes are distributed in the network, the more the search efficiency of the network can be enhanced.