Comprehensive analysis of the effect of structural parameters on erosion wear, structural stress, and deformation of high-pressure double-elbow in shale-gas fracturing.

In field hydraulic fracturing operation of shale gas development, the high pressure and large displacement liquid-particle two-phase fracturing fluid can be forced to change direction many times through high-pressure double-elbow, and be transported from the outlet pipeline of the fracturing pump to the main pipeline. The high-pressure double-elbow is prone to be affected by erosion wear and Fluid-Structure Interaction (FSI), resulting in perforation and fracture, posing a potential safety threat to field operation. In this study, we conducted the erosion wear experiments on 35CrMo steel used for high-pressure double-elbow in shale-gas fracturing. The erosion rates under different impact angles and flow velocities were obtained, and proposed a novel model of erosion prediction for high-pressure double-elbow. Then the numerical investigation was employed to conduct a comprehensive analysis of erosion wear, structural stress and deformation by the coupling of Computational Fluid Dynamics (CFD) and Finite Element Analysis (FEA). The effects of structural parameters such as connection straight pipe length, pipe inner diameter and fluid turning direction were discussed. The results indicate that with the increase of connection straight pipe length, the flow erosion decreases first then varies little, and the deformation gradually increases. Slight erosion wear but large structural stress and deformation in major inner diameter pipe. And the minimum degree of erosion and flow-induced deformation present with the fluid turning direction of double-elbow as 0°. The study can provide references for the design, installation and detection of high-pressure double-elbow and ensure safety in the process of shale gas fracturing.

Fiedler value: The cumulated dynamical contribution value of all edges in a complex network.

Fiedler value, as the minimal real part of (or the minimal) nonzero Laplacian eigenvalue, garners significant attention as a metric for evaluating network topology and its dynamics. In this paper, we address the quantification relation between Fiedler value and each edge in a directed complex network, considering undirected networks as a special case. We propose an approach to measure the dynamical contribution value of each edge. Interestingly, these contribution values can be both positive and negative, which are determined by the left and right Fiedler vectors. Further, we show that the cumulated dynamical contribution value of all edges is exactly the Fiedler value. This provides a promising angle on the Fiedler value in terms of dynamics and network structure. Therefore, the percentage of contribution of each edge to the Fiedler value is quantified. Numerical results reveal that network dynamics is significantly influenced by a small fraction of edges, say, one single directed edge contributes to over 90% of the Fiedler value in the Cat Cerebral Cortex network.

Diagnostic performance of metagenomic next-generation sequencing and conventional microbial culture for spinal infection: a retrospective comparative study.

PURPOSE: The study evaluated the diagnostic performance of metagenomic next-generation sequencing (mNGS) as a diagnostic test for biopsy samples from patients with suspected spinal infection (SI) and compared the diagnostic performance of mNGS with that of microbial culture. METHODS: All patients diagnosed with clinical suspicion of SI were enrolled, and data were collected through a retrospective chart review of patient records. Biopsy specimens obtained from each patient were tested via mNGS and microbial culture. Samples were enriched for microbial DNA using the universal DNA extraction kit, whole-genome amplified, and sequenced using MGISEQ-200 instrument. After Low-quality reads removed, the remaining sequences for microbial content were analyzed and aligned using SNAP and kraken2 tools. RESULTS: A total of 39 patients (19 men and 20 women) were deemed suitable for enrollment. The detection rate for pathogens of mNGS was 71.8% (28/39), which was significantly higher than that of microbial culture (23.1%, p = 0.016). Mycobacterium tuberculosis complex was the most frequently isolated. Using pathologic test as the standard reference for SI, thirty-one cases were classified as infected, and eight cases were considered aseptic. The sensitivity and specificity values for detecting pathogens with mNGS were 87.1% and 87.5%, while these rates were 25.8% and 87.5% with conventional culture. mNGS was able to detect 88.9% (8/9) of pathogens identified by conventional culture, with a genus-level sensitivity of 100% (8/8) and a species-level sensitivity of 87.5% (7/8). CONCLUSION: The present work suggests that mNGS might be superior to microbial culture for detecting SI pathogens.

Searching for Key Cycles in a Complex Network.

Searching for key nodes and edges in a network is a long-standing problem. Recently cycle structure in a network has received more attention. Is it possible to propose a ranking algorithm for cycle importance? We address the problem of identifying the key cycles of a network. First, we provide a more concrete definition of importance-in terms of Fiedler value (the second smallest Laplacian eigenvalue). Key cycles are those that contribute most substantially to the dynamical behavior of the network. Second, by comparing the sensitivity of Fiedler value to different cycles, a neat index for ranking cycles is provided. Numerical examples are given to show the effectiveness of this method.

Superdiffusion induced by complete structure in multiplex networks.

After the groundbreaking work by Gómez et al., the superdiffusion phenomenon on multiplex networks begins to attract researchers' attention. The emergence of superdiffusion means that the time scale of the diffusion process of the multiplex network is shorter than that of each layer. Using the optimization theory, the manuscript studies the greatest impact of one edge on the network diffusion speed. It is proved that by deleting any edge from a given network, the drop of the second smallest eigenvalue of its Laplacian matrix is at most 2. Based on the conclusion, the relation between the complete structure and the superdiffusible network is studied, and, further, some superdiffusion criteria on general duplex networks are proposed. Interestingly, the theoretical results indicate that the emergence of superdiffusion depends on the complete structure rather than the overlap one. Some numerical examples are shown to verify the effectiveness of the theoretical results.

Occurrence of super-diffusion in two-layer networks.

Super-diffusion is a phenomenon that can be observed in multilayer networks, which describes that the diffusion in a multilayer network is faster than that in the fastest individual layer. In most studies of super-diffusion on two-layer networks, many researchers have focused on the overlap of edges in the two layers and the mode of interlayer connectivity. We discover that the occurrence of super-diffusion in two-layer networks is not necessarily related to the overlap degree. In particular, in a two-layer network, sparse topological structures of individual layers are more beneficial to the occurrence of super-diffusion than dense topological structures. Additionally, similar diffusion abilities of both layers favor super-diffusion. The density of interlayer edges and interlayer connection patterns also influence the occurrence of super-diffusion. This paper offers suggestions to improve the diffusion ability in two-layer networks, which can facilitate the selection of practical information transmission paths between different systems and optimize the design of the internal framework of a company composed of multiple departments.

Adaptive Exponential Synchronization of Complex Networks With Nondifferentiable Time-Varying Delay.

In recent years, the adaptive exponential synchronization (AES) problem of delayed complex networks has been extensively studied. Existing results rely heavily on assuming the differentiability of the time-varying delay, which is not easy to verify in reality. Dealing with nondifferentiable delay in the field of AES is still a challenging problem. In this brief, the AES problem of complex networks with general time-varying delay is addressed, especially when the delay is nondifferentiable. A delay differential inequality is proposed to deal with the exponential stability of delayed nonlinear systems, which is more general than the widely used Halanay inequality. Next, the boundedness of the adaptive control gain is theoretically proved, which is neglected in much of the literature. Then, the AES criteria for networks with general delay are established for the first time by using the proposed inequality and the boundedness of the control gain. Finally, an example is given to demonstrate the effectiveness of the theoretical results.

Synchronization of Complex Networks With Nondifferentiable Time-Varying Delay.

In this article, we investigate the synchronization of complex networks with general time-varying delay, especially with nondifferentiable delay. In the literature, the time-varying delay is usually assumed to be differentiable. This assumption is strict and not easy to verify in engineering. Until now, the synchronization of networks with nondifferentiable delay through adaptive control remains a challenging problem. By analyzing the boundedness of the adaptive control gain and extending the well-known Halanay inequality, we solve this problem and establish several synchronization criteria for networks under the centralized adaptive control and networks under the decentralized adaptive control. Particularly, the boundedness of the centralized adaptive control gain is theoretically proved. Numerical simulations are provided to verify the theoretical results.

Topology Identification of Multilink Complex Dynamical Networks via Adaptive Observers Incorporating Chaotic Exosignals.

Topology identification of complex networks is an important and meaningful research direction. In recent years, the topology identification method based on adaptive synchronization has been developed rapidly. However, a critical shortcoming of this method is that inner synchronization of a network breaks the precondition of linear independence and leads to the failure of topology identification. Hence, how to identify the network topology when possible inner synchronization occurs within the network has been a challenging research issue. To solve this problem, this article proposes improved topology identification methods by regulating the original network to synchronize with an auxiliary network composed of isolated chaotic exosystems. The proposed methods do not require the sophisticated assumption of linear independence. The topology identification observers incorporating a series of isolated chaotic exosignals can accurately identify the network structure. Finally, numerical simulations show that the proposed methods are effective to identify the structure of a network even with large weights of edges and abundant connections between nodes.

Synchronizability of two-layer correlation networks.

This study investigates the synchronizability of a typical type of two-layer correlation networks formed by two regular networks interconnected with two interlayer linking patterns, namely, positive correlation (PC) and negative correlation (NC). To analyze the network's stability, we consider the analytical expressions of the smallest non-zero and largest eigenvalues of the (weighted) Laplacian matrix as well as the linking strength and the network size for two linking patterns. According to the master stability function, the linking patterns, the linking strength, and the network size associated with two typical synchronized regions exhibit a profound influence on the synchronizability of the two-layer networks. The NC linking pattern displays better synchronizability than the PC linking pattern with the same set of parameters. Furthermore, for the two classical synchronized regions, the networks have optimal intralayer and interlayer linking strengths that maximize the synchronizability while minimizing the required cost. Finally, numerical results verify the validity of the theoretical analyses. The findings based on the representative two-layer correlation networks provide the basis for maximizing the synchronizability of general multiplex correlation networks.

Superdiffusion criteria on duplex networks.

Diffusion processes widely exist in nature. Some recent papers concerning diffusion processes focus their attention on multiplex networks. Superdiffusion, a phenomenon by which diffusion processes converge to equilibrium faster on multiplex networks than on single networks in isolation, may emerge because diffusion can occur both within and across layers. Some studies have shown that the emergence of superdiffusion depends on the topology of multiplex networks if the interlayer diffusion coefficient is large enough. This paper proposes some superdiffusion criteria relating to the Laplacian matrices of the two layers and provides a construction mechanism for generating a superdiffusible two-layered network. The method we proposed can be used to guide the discovery and construction of superdiffusible multiplex networks without calculating the second smallest Laplacian eigenvalues.

Synchronizability of double-layer dumbbell networks.

Synchronization of multiplex networks has been a topical issue in network science. Dumbbell networks are very typical structures in complex networks which are distinguished from both regular star networks and general community structures, whereas the synchronous dynamics of a double-layer dumbbell network relies on the interlink patterns between layers. In this paper, two kinds of double-layer dumbbell networks are defined according to different interlayer coupling patterns: one with the single-link coupling pattern between layers and the other with the two-link coupling pattern between layers. Furthermore, the largest and smallest nonzero eigenvalues of the Laplacian matrix are calculated analytically and numerically for the single-link coupling pattern and also obtained numerically for the two-link coupling pattern so as to characterize the synchronizability of double-layer dumbbell networks. It is shown that interlayer coupling patterns have a significant impact on the synchronizability of multiplex systems. Finally, a numerical example is provided to verify the effectiveness of theoretical analysis. Our findings can facilitate company managers to select optimal interlayer coupling patterns and to assign proper parameters in terms of improving the efficiency and reducing losses of the whole team.

The combination of targeted vaccination and ring vaccination.

Complex networks have become an important tool for investigating epidemic dynamics. A widely concerned research field for epidemics is to develop and study mitigation strategies or control measures. In this paper, we devote our attention to ring vaccination and targeted vaccination and consider the combination of them. Based on the different roles ring vaccination plays in the mixed strategy, the whole parameter space can be roughly divided into two regimes. In one regime, the mixed strategy performs poorly compared with targeted vaccination alone, while in the other regime, the addition of ring vaccination can improve the performance of targeted vaccination. This result gives us the more general and overall comparison between targeted and ring vaccination. In addition, we construct a susceptible-infected-recovered epidemic model coupled with the immunization dynamics on random networks. The comparison between stochastic simulations and numerical simulations confirms the validity of the model we propose.

A New Method for Topology Identification of Complex Dynamical Networks.

Topology identification of complex dynamical networks received extensive attention in the past decade. Most existing studies rely heavily on the linear independence condition (LIC). We find that a critical step in using this condition is not rigorous. Besides, it is difficult to verify this condition. Without regulating the original network, possible identification failure caused by network synchronization cannot be avoided. In this paper, we propose a new method to overcome these shortcomings. We add a regulation mechanism to the original network and construct an auxiliary network consisting of isolated nodes. Along with the outer synchronization between the regulated network and the auxiliary network, we show that the original network can be identified. Our method can avoid identification failure caused by network synchronization. Moreover, we show that there is no need to check the LIC. We finally provide some examples to demonstrate that our method is reliable and has good performances.

The effect of behavior of wearing masks on epidemic dynamics.

Recently, COVID-19 has attracted a lot of attention of researchers from different fields. Wearing masks is a frequently adopted precautionary measure. In this paper, we investigate the effect of behavior of wearing masks on epidemic dynamics in the context of COVID-19. At each time, every susceptible individual chooses whether to wear a mask or not in the next time step, which depends on an evaluation of the potential costs and perceived risk of infection. When the cost of infection is high, the majority of the population choose to wear masks, where global awareness plays a significant role. However, if the mask source is limited, global awareness may give rise to a negative result. In this case, more mask source should be allocated to the individuals with high risk of infection.

Statistical and network analysis of 1212 COVID-19 patients in Henan, China.

BACKGROUND: COVID-19 is spreading quickly all over the world. Publicly released data for 1212 COVID-19 patients in Henan of China were analyzed in this paper. METHODS: Various statistical and network analysis methods were employed. RESULTS: We found that COVID-19 patients show gender (55% vs 45%) and age (81% aged between 21 and 60) preferences; possible causes were explored. The estimated average, mode and median incubation periods are 7.4, 4 and 7 days. Incubation periods of 92% of patients were no more than 14 days. The epidemic in Henan has undergone three stages and has shown high correlations with the numbers of patients recently returned from Wuhan. Network analysis revealed that 208 cases were clustering infected, and various People's Hospitals are the main force in treating COVID-19. CONCLUSIONS: The incubation period was statistically estimated, and the proposed state transition diagram can explore the epidemic stages of emerging infectious disease. We suggest that although the quarantine measures are gradually working, strong measures still might be needed for a period of time, since ∼7.45% of patients may have very long incubation periods. Migrant workers or college students are at high risk. State transition diagrams can help us to recognize the time-phased nature of the epidemic. Our investigations have implications for the prevention and control of COVID-19 in other regions of the world.

Master stability functions for complete, intralayer, and interlayer synchronization in multiplex networks of coupled Rössler oscillators.

Synchronization phenomena are of broad interest across disciplines and increasingly of interest in a multiplex network setting. For the multiplex network of coupled Rössler oscillators, here we show how the master stability function, a celebrated framework for analyzing synchronization on a single network, can be extended to certain classes of multiplex networks with different intralayer and interlayer coupling functions. We derive three master stability equations that determine, respectively, the necessary regions of complete synchronization, intralayer synchronization, and interlayer synchronization. We calculate these three regions explicitly for the case of a two-layer network of Rössler oscillators and show that the overlap of the regions determines the type of synchronization achieved. In particular, if the interlayer or intralayer coupling function is such that the interlayer or intralayer synchronization region is empty, complete synchronization cannot be achieved regardless of the coupling strength. Furthermore, for any network structure, the occurrence of intralayer and interlayer synchronization depends mainly on the coupling functions of nodes within a layer and across layers, respectively. Our mathematical analysis requires that the intralayer and interlayer supra-Laplacians commute. But, we show this is only a sufficient, and not necessary, condition and that the results can be applied more generally.

Cluster synchronization: From single-layer to multi-layer networks.

Cluster synchronization is a very common phenomenon occurring in single-layer complex networks, and it can also be observed in many multilayer networks in real life. In this paper, we study cluster synchronization of an isolated network and then focus on that of the network when it is influenced by an external network. We mainly explore how the influence layer impacts the cluster synchronization of the interest layer in a multilayer network. Considering that the clusters are changeable, we introduce a term called "cluster synchronizability" to measure the ability of a network to reach cluster synchronization. Since cluster synchronizability is intimately associated with the structure of the coupled external layer, we consider community networks and networks with different densities as the coupled layer. Besides the topology structure, the connection between two layers may also have an influence on the cluster synchronization of the interest layer. We study three different patterns of connection, including typical positive correlation, negative correlation, and random correlation and find that they all have a certain influence. However, the general theoretical analysis of cluster synchronization on multilayer networks is still a challenging topic. In this paper, we mainly use numerical simulations to discuss cluster synchronization.

Pinning Synchronization of Multiplex Delayed Networks With Stochastic Perturbations.

As the monoplex network has its limitations in describing the real world, a new framework called the multiplex network is put forward and has received much attention in recent years. This paper focuses on synchronization of the multiplex network with multiple delays and stochastic perturbations. Due to the complexity, pinning control of the multiplex network is of particular interest. Based on the LaSalle-type invariance principle for stochastic differential delay equations and the Lyapunov stability theory, some control schemes and synchronization criteria are obtained. It is concluded that under some mild conditions, one can determine which nodes should be pinned in a multiplex network. In addition, it is found that the number of pinned nodes increases with the varying interval of noise and time delay, and decreases with the varying interval of intralayer coupling strength. Some two-layer and three-layer networks are employed to validate the effectiveness of the proposed algorithm.

Synthesis of Diverse Boron-Handled N-Heterocycles via Radical Borylative Cyclization of N-Allylcyanamides.

A synthetic method based on radical borylation/cyclization cascades of N-allylcyanamides was developed to construct diverse boron-substituted N-heterocycles. In the reaction process, the N-heterocyclic carbene-boryl radical underwent a chemo- and regioselective addition to the alkene moiety, followed by cyclization with the N-cyano group. The resulting amide-iminyl radical intermediates underwent further reactions to afford various boron-tethered N-heterocyclic molecules. Further transformations to access synthetically useful building blocks were also demonstrated.