Spatiotemporal modeling of water inrush spreading in mine roadway networks

Water inrush accidents seriously threaten underground mining production, so the accurate prediction of the spreading process of water inrush is essential for the formulation of water-inrush-control plans and rescue schemes. This paper proposes a spatiotemporal model based on pipe-flow theory to simulate the spreading process of water inrush in mine roadway networks. The energy-loss term is added to this model to improve the simulation accuracy in bifurcated roadways, and pumps and water-blocking equipment are considered in controlling the spreading process of water inrush. Through experimental case studies, the simulation results and the function of the energy-loss term are verified. A sensitivity analysis is then carried out to assess the impact of the model parameters. The results show that the model outputs are most sensitive to the roadway length, cross-section width, and energy-loss coefficient. The model exhibited maximal sensitivity to the geometric parameters compared with the hydraulic parameters. Furthermore, the spreading process of a real water inrush in a coal mine in North China is simulated, and the water-inrush-control measures are evaluated. The overall results indicate that the proposed spatiotemporal model accurately predicts the spreading process of water inrush and is thus applicable to large-scale mine roadway networks.

Exploring S-shape curves and heterogeneity effects of rumor spreading in online collective actions

<abstract> <p>Nowadays online collective actions are pervasive, such as the rumor spreading on the Internet. The observed curves take on the S-shape, and we focus on evolutionary dynamics for S- shape curves of online rumor spreading. For agents, key factors, such as internal aspects, external aspects, and hearing frequency jointly determine whether to spread it. Agent-based modeling is applied to capture micro-level mechanism of this S-shape curve. We have three findings: (a) Standard S-shape curves of spreading can be obtained if each agent has the zero threshold; (b) Under zero-mean thresholds, as heterogeneity (SD) grows from zero, S-shape curves with longer right tails can be obtained. Generally speaking, stronger heterogeneity comes up with a longer duration; and (c) Under positive mean thresholds, the spreading curve is two-staged, with a linear stage (first) and nonlinear stage (second), but not standard S-shape curves either. From homogeneity to heterogeneity, the spreading S-shaped curves have longer right tail as the heterogeneity grows. For the spreading duration, stronger heterogeneity usually brings a shorter duration. The effects of heterogeneity on spreading curves depend on different situations. Under both zero and positive-mean thresholds, heterogeneity leads to S-shape curves. Hence, heterogeneity enhances the spreading with thresholds, but it may postpone the spreading process with homogeneous thresholds.</p> </abstract>

Influence fast or later: two types of influencers in social networks

In real-world networks, there usually exist a small set of nodes that play an important role in the structure and function of networks. Those vital nodes can influence most other nodes in the network via a spreading process. While most of the existing works focused on vital nodes that can maximize the spreading size in the final stage, which we call final influencers, recent work proposed the idea of fast influencers, which emphasizes nodes’ spreading capacity at the early stage. Despite the recent surge of efforts in identifying these two types of influencers in networks, there remained limited research on untangling the differences between fast influencers and final influencers. In this paper, we first distinguish the two types of influencers: fast-only influencers and final-only influencers. The former is defined as individuals who can achieve a high spreading effect at the early stage but lose their superiority in the final stage, and the latter are those individuals that fail to exhibit a prominent spreading performance at the early stage but influence a large fraction of nodes at the final stage. Further experiments based on eight empirical datasets, we reveal the key differences between the two types of influencers concerning their spreading capacity and the local structures. We also analyze how network degree assortativity influences the fraction of the proposed two types of influencers. The results demonstrate that with the increase of degree assortativity, the fraction of the fast-only influencers decreases, which indicates that more fast influencers tend to keep their superiority at the final stage. Our study provides insights into the differences and evolution of different types of influencers and has important implications for various empirical applications, such as advertisement marketing, and epidemic suppressing.

Stochastic Simulation Algorithm for effective spreading dynamics on Time-evolving Adaptive NetworX (SSATAN-X)

Modelling and simulating the dynamics of pathogen spreading has been proven crucial to inform public heath decisions, containment strategies, as well as cost-effectiveness calculations. Pathogen spreading is often modelled as a stochastic process that is driven by pathogen exposure on time-evolving contact networks. In adaptive networks, the spreading process depends not only on the dynamics of a contact network, but vice versa, infection dynamics may alter risk behaviour and thus feed back onto contact dynamics, leading to emergent complex dynamics. However, stochastic simulation of pathogen spreading processes on adaptive networks is currently computationally prohibitive. In this manuscript, we propose SSATAN-X, a new algorithm for the accurate stochastic simulation of pathogen spreading on adaptive networks. The key idea of SSATAN-X is to only capture the contact dynamics that are relevant to the spreading process. We show that SSATAN-X captures the contact dynamics and consequently the spreading dynamics accurately. The algorithm achieves a > 10 fold speed-up over the state-of-art stochastic simulation algorithm (SSA). The speed-up with SSATAN-X further increases when the contact dynamics are fast in relation to the spreading process, i.e. if contacts are short-lived and per-exposure infection risks are small, as applicable to most infectious diseases. We envision that SSATAN-X may extend the scope of analysis of pathogen spreading on adaptive networks. Moreover, it may serve to create benchmark data sets to validate novel numerical approaches for simulation, or for the data-driven analysis of the spreading dynamics on adaptive networks. A C++ implementation of the algorithm is available https://github.com/nmalysheva/SSATAN-X

Comparing information diffusion mechanisms by matching on cascade size

Do some types of information spread faster, broader, or further than others? To understand how information diffusions differ, scholars compare structural properties of the paths taken by content as it spreads through a network, studying so-called cascades. Commonly studied cascade properties include the reach, depth, breadth, and speed of propagation. Drawing conclusions from statistical differences in these properties can be challenging, as many properties are dependent. In this work, we demonstrate the essentiality of controlling for cascade sizes when studying structural differences between collections of cascades. We first revisit two datasets from notable recent studies of online diffusion that reported content-specific differences in cascade topology: an exhaustive corpus of Twitter cascades for verified true- or false-news content by Vosoughi et al. [S. Vosoughi, D. Roy, S. Aral. Science 359, 1146–1151 (2018)] and a comparison of Twitter cascades of videos, pictures, news, and petitions by Goel et al. [S. Goel, A. Anderson, J. Hofman, D. J. Watts. Manage. Sci. 62, 180–196 (2016)]. Using methods that control for joint cascade statistics, we find that for false- and true-news cascades, the reported structural differences can almost entirely be explained by false-news cascades being larger. For videos, images, news, and petitions, structural differences persist when controlling for size. Studying classical models of diffusion, we then give conditions under which differences in structural properties under different models do or do not reduce to differences in size. Our findings are consistent with the mechanisms underlying true- and false-news diffusion being quite similar, differing primarily in the basic infectiousness of their spreading process.

Modelling Forest Fires Using Complex Networks

Forest fires have been a major threat to the environment throughout history. In order to mitigate its consequences, we present, in a first of a series of works, a mathematical model with the purpose of predicting fire spreading in a given land portion divided into patches, considering the area and the rate of spread of each patch as inputs. The rate of spread can be estimated from previous knowledge on fuel availability, weather and terrain conditions. We compute the time duration of the spreading process in a land patch in order to construct and parametrize a landscape network, using cellular automata simulations. We use the multilayer network model to propose a network of networks at the landscape scale, where the nodes are the local patches, each with their own spreading dynamics. We compute some respective network measures and aim, in further work, for the establishment of a fire-break structure according to increasing accuracy simulation results.

Wetting and Spreading of AgCuTi on Selective Laser-Melted Ti-6Al-4V

Selective laser melting (SLM) can be used to manufacture complex parts, however, it is difficult to make large parts due to the size limitation of the SLM equipment. In application, smaller selective laser-melted (SLMed) Ti-6Al-4V (TC4) parts can be brazed or welded to form larger components. In the brazing, AgCuTi is often used to braze TC4. However, the wettability of AgCuTi on the SLMed TC4 should be evaluated before joining the SLMed TC4 parts. As a result, wetting and spreading tests and brazing experiments should be undertaken to successfully join the SLMed TC4 parts. In this study, a LINKAM TS 1500 high-temperature hot stage was used to test the brazability of the AgCuTi on the surface of SLMed TC4. Different temperatures and dwell times were used: (i) 850 °C 900 °C and 950 °C, holding for 120 s, were used to study the temperature effects; (ii) 20 s, 120 s and 200 s were used at 850 °C to study the dwell time effects. The R~t model was used to describe the wetting and spreading process. The results of this study can provide basic data for the joining of SLMed TC4 in industry.

Statistical analysis of the community lockdown for COVID-19 pandemic

As the global pandemic of the COVID-19 continues, the statistical modeling and analysis of the spreading process of COVID-19 have attracted widespread attention. Various propagation simulation models have been proposed to predict the spread of the epidemic and the effectiveness of related control measures. These models play an indispensable role in understanding the complex dynamic situation of the epidemic. Most existing work studies the spread of epidemic at two levels including population and agent. However, there is no comprehensive statistical analysis of community lockdown measures and corresponding control effects. This paper performs a statistical analysis of the effectiveness of community lockdown based on the Agent-Level Pandemic Simulation (ALPS) model. We propose a statistical model to analyze multiple variables affecting the COVID-19 pandemic, which include the timings of implementing and lifting lockdown, the crowd mobility, and other factors. Specifically, a motion model followed by ALPS and related basic assumptions is discussed first. Then the model has been evaluated using the real data of COVID-19. The simulation study and comparison with real data have validated the effectiveness of our model.

The histone chaperone FACT facilitates heterochromatin spreading through regulation of histone turnover and H3K9 methylation states

Heterochromatin formation requires three distinct steps: nucleation, self-propagation (spreading) along the chromosome, and faithful maintenance after each replication cycle. Impeding any of those steps induces heterochromatin defects and improper gene expression. The essential histone chaperone FACT has been implicated in heterochromatin silencing, however, the mechanisms by which FACT engages in this process remain opaque. Here, we pin-pointed its function to the heterochromatin spreading process. FACT impairment reduces nucleation-distal H3K9me3 and HP1/Swi6 accumulation at subtelomeres and de-represses genes in the vicinity of heterochromatin boundaries. FACT promotes spreading by repressing heterochromatic histone turnover, which is crucial for the H3K9me2 to me3 transition that enables spreading. FACT mutant spreading defects are suppressed by removal of the H3K9 methylation antagonist Epe1 via nucleosome stabilization. Together, our study identifies FACT as a histone chaperone that specifically promotes heterochromatin spreading and lends support to the model that regulated histone turnover controls the propagation of epigenetic marks.