A Multi-Information Spreading Model for One-Time Retweet Information in Complex Networks.

In the realm of online social networks, the spreading of information is influenced by a complex interplay of factors. To explore the dynamics of one-time retweet information spreading, we propose a Susceptible-Infected-Completed (SIC) multi-information spreading model. This model captures how multiple pieces of information interact in online social networks by introducing inhibiting and enhancement factors. The SIC model considers the completed state, where nodes cease to spread a particular piece of information after transmitting it. It also takes into account the impact of past and present information received from neighboring nodes, dynamically calculating the probability of nodes spreading each piece of information at any given moment. To analyze the dynamics of multiple information pieces in various scenarios, such as mutual enhancement, partial competition, complete competition, and coexistence of competition and enhancement, we conduct experiments on BA scale-free networks and the Twitter network. Our findings reveal that competing information decreases the likelihood of its spread while cooperating information amplifies the spreading of mutually beneficial content. Furthermore, the strength of the enhancement factor between different information pieces determines their spread when competition and cooperation coexist. These insights offer a fresh perspective for understanding the patterns of information propagation in multiple contexts.

Recursive traffic percolation on urban transportation systems.

This paper proposes a recursive traffic percolation framework to capture the dynamics of cascading failures and analyze potential overloaded bottlenecks. In particular, compared to current work, the influence of external flow is considered, providing a new perspective for the study of regional commuting. Finally, we present an empirical study to verify the accuracy and effectiveness of our framework. Further analysis indicates that external flows from different regions affect the network. Our work requires only primary data and verifies the improvement of the functional network.

Numerical Investigation of Fine Particulate Matter Aggregation and Removal by Water Spray Using Swirling Gas Flow.

In this paper, a mathematical model based on the two-fluid frame model coupled with the population balance model which considers the aggregation of particles and droplets in detail for cyclonic spray dedusting is proposed. The model is applied to study the characteristics of multiphase flow field and the effects of the gas velocity, spray volume, and particle concentration on the removal efficiency. In addition, the simulation results are verified by the experimental data. The results suggest that the turbulence kinetic energy increases near the wall as the inlet velocity increases, and the spray region increases as the spray volume increases. This is conducive to turbulent mixing of the particles and droplets, and the agglomeration efficiency of the particles is improved, so the particle size increases, and the particle removal efficiency increases to 99.7% by simulation results are within the allowable range of error (about 99-99.5% in dedusting efficiency by experimental data). As the particle concentration increases, the particle removal efficiency initially increases, then decreases and reaches the highest value at 2 g/m3, which is due to the limited adsorption efficiency of the spray droplets. The results are helpful for providing a theoretical basis for spray to promote agglomeration of particles and improving the dust removal efficiency in the swirl field.

CFD Modeling of Ventilation and Dust Flow Behavior in Polishing and the Design of an Innovative Wet Dust Removal System.

Fine aluminum dust pollution in the polishing process was detected during a field survey. To obtain a fundamental understanding of the airflow patterns and the fine dust dispersion characteristics during a polishing process, computational fluid dynamics simulations were first performed to analyze the data collected in field measurements. The inappropriate ventilation arrangement and lack of effective dust control measures were identified as the main reasons for the high dust exposure levels (in excess of 1000 µg/m3). Simulation results showed that inhalable dust particles (PM10) could be significantly diluted at the operator's breathing level by adding a supply air inlet above the operating area. Moreover, dry dust collection systems create a risk of aluminum dust explosion accidents. An innovative design of wet dust removal system which could mitigate the occurrence of dust explosions was proposed and then implemented on site. An independent field dust assessment showed that a reduction of fine dust particles up to 95% in the worker's breathing area and the fine dust in the vents was reduced to 80%. Therefore, the proposed strategies are implemented immediately to address the combustible dust in the polishing working environment and can provide guidance for operators.

Optimal transport in time-varying small-world networks.

The time-order of interactions, which is regulated by some intrinsic activity, surely plays a crucial role regarding the transport efficiency of transportation systems. Here we study the optimal transport structure by measure of the length of time-respecting paths. Our network is built from a two-dimensional regular lattice, and long-range connections are allocated with probability P(ij)∼r(ij)(-α), where r(ij) is the Manhattan distance. By assigning each shortcut an activity rate subjected to its geometric distance τ(ij)∼r(ij)(-C), long-range links become active intermittently, leading to the time-varying dynamics. We show that for 0

Origin of Gibrat law in Internet: asymmetric distribution of the correlation.

Although Gibrat's law and its generalized versions have been widely used, the organizing principle behind its phenomenological theory has been poorly studied for network-structured systems. More important, its fluctuation behavior, which contradicts the prediction of the preferential attachment (PA), indicates a nontrivial mechanism that goes beyond our present knowledge based on the traditional mean-field approach. Here, we take advantage of the rich data of the Internet and aim to identify the origin of Gibrat's law by studying the empirical fluctuation behavior. We show how the correlation between the fluctuations of the node degree increment affects the dynamics of the network. Specifically, if the distribution of the correlation is symmetric, the network evolves as the classical PA, while if such symmetry breaks, the fluctuation becomes macroscopically positively correlated and contributes to the emergence of Gibrat's law. These results indicate a local collective increase in the actual network evolution, which provides a new paradigm and understanding of the related microcosmic dynamics.