Propagation Probability Research Articles

A novel algorithm for identifying influential nodes in complex networks based on local propagation probability model

Identifying influential nodes in complex networks is an essential research issue in network science since it may attribute to understand network structure and function. Majority of existing methods have been established by considering topological characteristics of networks. In this paper, we put forward a novel algorithm based on the Local Propagation Probability (LPP) model for identifying influential nodes in complex networks. The core idea of LPP algorithm is that the nodal influence is measured by total comprehensive scores of neighbor nodes within its three level neighborhood. Specially, the comprehensive score is calculated from three dimensions, namely the propagation influence score between different order neighbors, the propagation influence score in the same order neighbors and the hierarchical structure information of nodes. To validate the performance and applicability of the proposed algorithm, LPP is compared with eight state-of-the-art and competitive algorithms on nine real-world networks. Experimental results demonstrate that LPP performs better in terms of ranking accuracy, effectiveness, top-k nodes and distinguishing ability. The low time complexity allows LPP to be applied to large-scale sparse networks.

Probabilistic modeling of threshold stress intensity factor for fatigue endurance reliability prediction

This study develops a probabilistic model for the threshold stress intensity factor range, which is a critical parameter in infinite fatigue life design under material flaws. The model is based on the proposed concept of probability of propagation in the probabilistic framework, allowing for deriving the probability density function of the threshold intensity factor range. The uncertainty in fatigue crack growth can naturally be incorporated into the resulting distribution. By further introducing the derived distribution into the Kitagawa–Takahashi diagram, the fatigue endurance reliability model can be established in a rational manner. With the first-order asymptotic approximation, the analytical form of fatigue endurance reliability index is obtained. The usefulness of the overall method is demonstrated using realistic engineering application examples.

Industrial Noise Mapping: Literature Review, Designed for New Plant Operation

This paper review focuses onto industrial mapping published by researchers in different industrial sectors from different countries published in past 10-year time aimed at mapping software usage and mitigation plans implemented. The review found out that ArcGIS and SurferPLAN were commonly utilized in visualizing the industry measured noise into the contour map and the noise mapping study in each specific discrete study area could be used as initial document or basis for decision maker or town city planning to plan and execute the noise mitigation measures. In the industrial expansion planning, on the other hand, it is obvious that the previous study of noise monitoring and noise mapping could be used as basis of predicting the noise level of any planned machinery installation, heavy equipment operation, or even building construction and its probability of noise propagation as well as predicted noise control measures.

Understanding the role of catchment and climate characteristics in the propagation of meteorological to hydrological drought

Hydrological and meteorological droughts are complex hazards that cover a wide spatiotemporal spectrum and can affect the economic, environmental, and social sectors. However, it is challenging to characterize hydrometeorological drought and its propagation from precipitation deficits to soil moisture, discharge, and groundwater deficits and to ascertain to what extent catchment characteristics may affect drought features. This research explores the link between hydrometeorological drought characteristics and catchment characteristics. It investigates their propagation from meteorological to hydrological drought conditions in nine selected catchments from the Awash basin in Ethiopia using standardized indices of precipitation (SPI), soil moisture deficits (SPEI), streamflow (SRI), and base flow (SBI). The study results indicate that the propagation of meteorological to agricultural and hydrological droughts increases with increasing aggregation time scale and has a proportionally higher probability of propagation for moderate, severe, and extreme droughts. Across all catchments, the average lag time between hydrological and meteorological drought magnitude occurrence is 4–6 months, with lag times likely to increase by 7–8 months at a higher aggregation time scale. Meteorological drought has a 0.5 higher probability of occurrence than hydrological drought in a given period. In contrast, it has a lower (0.3) probability of the surface deficit becoming a groundwater deficit. At a 3-month cumulative time scale, the probability of propagating meteorological drought using the SPI to hydrological drought using the SRI is higher than that of propagating hydrological drought using the SBI. Therefore, the results suggest that while the propagation of meteorological droughts (SPI and SPEI) to hydrological droughts (SRI and SBI) slightly increases with aggregation time, the time taken for precipitation to become apparent in hydrological variables increases. The results confirmed that the probability of meteorological drought leading to hydrological drought progression is critical and needs to be considered in water resource management and planning.

Extending influence maximization by optimizing the network topology

Influence maximization has recently obtained significant attention in advertising and rumor control on a social network. However, most existing works on influence maximization focus on mining seed nodes with high influence, while the timeliness of information diffusion in dynamic social networks has not been fully explored, resulting in the diffusion effect is not up to the expected level. To fill this gap, we investigate how to maximize the influence spread range of seed set A by adding k latent edges to dynamic network Gt, which is called Dynamic Edge Addition (DEA). For this purpose, we design an Activation Probability-aware (AP) framework for adding edges to the network. Specifically, AP contains three modules: graph generation, influence estimation of nodes, and edge addition. The module of graph generation is used to obtain graph structure. In the module of influence estimation, we employ a baseline Influence Maximization via Martingales (IMM) algorithm and then improve it to make the seed more effective by setting a limit to the traversal depth of sampling. To capture the dynamic temporal changes of the network as soon as possible, we dynamically update the influence of nodes according to the propagation probability of the path. In the module of edge addition, we propose a novel strategy of edge addition by finding the shortest path length between the potential users and seed users, and realize the strategy in linear time. Extensive experiments on real-world datasets are conducted and the results show the superiority of the AP over other baselines.

Study of Flame Kernel Development at High EGR and High Flow Speed Using Conventional Spark Igniter and Non-Thermal Plasma Under Gasoline Engine Relevant Conditions

ABSTRACT As a novel ignition approach, nanosecond repetitively pulsed discharge (NRPD)-based non-thermal plasma offers significant benefits such as low energy consumption, and lean operating conditions. However, there is no investigation conducted on the early flame kernel formation and development induced by non-thermal plasma. Therefore, in this paper, experiments of conventional spark and non-thermal plasma ignition systems in constant volume combustion chamber (CVCC) are conducted under gasoline engine relevant conditions: wide initial ambient pressures (6.5, 8.3, 11.3 bar), a range of equivalence ratios (0.7–1.0), EGR rates (10−25%), and cross flow speeds (0–30 m/s). The discharge energy of non-thermal plasma is of around 210 mJ, while a single spark event can generate energy from 65 mJ to 83 mJ depending on ambient conditions. A consecutive spark strategy is adopted to guarantee comparable total input energy to non-thermal plasma. The ignition delay and combustion phase obtained from the chamber pressure history are calculated. In the meanwhile, flame kernel radius, flame propagation rate, and flame front length ratio via schlieren images are quantified and analyzed. Results showed that the flame initiated by non-thermal plasma can maintain a robust flame kernel and propagates fast. Under high-speed cross flow and high EGR rate conditions, non-thermal plasma can maintain a robust flame kernel at early stage supporting the initial flame kernel to survive and improve its ignition probability. Considering the effects of the EGR ratio and high-speed cross flow on flame kernel development, non-thermal plasma ignition can efficiently enhance the flame propagation and ignition probability. It is also concluded that non-thermal plasma can successfully ignite under lean (equivalence ratio between 0.7–1.0), high-diluted mixture (25% EGR), and high-speed cross flow of 30 m/s within the range of current study.

Inferring the stability of concentrated emulsions from droplet configuration information

When droplets are tightly packed in a 2D microchannel, coalescence of a pair of droplets can trigger an avalanche of coalescence events that propagate through the entire emulsion. This propagation is found to be stochastic, i.e. every coalescence event does not necessarily trigger another. To study how the local probabilistic propagation affects the dynamics of the avalanche, as a whole, a stochastic agent based model is used. Taking as input, i) how the droplets are packed (configuration) and ii) a measure of local probabilistic propagation (experimentally derived; function of fluid and other system parameters), the model predicts the expected size distribution of avalanches. In this article, we investigate how droplet configuration affects the avalanche dynamics. We find the mean size of these avalanches to depend non-trivially on how droplets are packed together. Large variations in the avalanche dynamics are observed when droplet packing are different, even when the other system properties (number of droplets, fluid properties, channel geometry, etc.) are kept constant. Bidisperse emulsions show less variation in the dynamics and they are surprisingly more stable than monodisperse emulsions. To get a systems-level understanding of how a given droplet-configuration either facilitates or impedes the propagation of an avalanche, we employ a graph-theoretic analysis, where emulsions are expressed as graphs. We find that the properties of the underlying graph, namely the mean degree and the algebraic connectivity, are well correlated with the observed avalanche dynamics. We exploit this dependence to derive a data-based model that predicts the expected avalanche sizes from the properties of the graph.

Characterization of the Propagation of Meteorological Drought Using the Copula Model

Drought is a phenomenon that is caused by several factors and can be divided into meteorological drought, agricultural drought, hydrological drought, and socioeconomic drought. In this study, the characteristics of propagating from meteorological drought to agricultural (or hydrological) drought in the Andong Dam basin and Hapcheon Dam basin located in the Nakdong River basin in Korea were investigated. Standardized precipitation index (SPI), standardized soil moisture index (SMI), and standardized runoff index (SRI) were used to characterize meteorological, agricultural, and hydrological droughts, respectively. SPI-m (1–12) on various timescales and SMI-1 (or SRI-1) were selected as drought propagation timeseries, such that a correlation analysis was performed to evaluate the correlation and propagation time between meteorological and agricultural (or hydrological) drought. Propagation probability was quantified using a copula-based model. The correlation between meteorological and agricultural (or hydrological) droughts was not significantly affected by seasons. A relatively strong correlation was found in summer. A relatively weak correlation was shown in autumn. In addition, it was found that there was a difference in correlation between the Andong Dam basin and the Hapcheon Dam basin. On the other hand, in both watersheds, the propagation time was as long as 2 to 4 months in spring and decreased to 1 month in summer.

Grazing Intensity Effects on Fire Ignition Risk and Spread in Sagebrush Steppe

Wildfire activity is accelerating on many rangelands worldwide, yet the potential for grazing to be used as a fire management tool remains largely unknown. Particularly, little is known about the influence of grazing on ignition and initial spread of fire, as well as how these vary by differences in grazing management. We investigated effects of grazing intensity (light, moderate, high) on fuel characteristics, fire ignition, and initial spread during the wildfire season in a native-dominated shrub steppe in eastern Oregon. We found that differences in grazing intensity have differential effects on fuel profiles (cover, height, moisture, biomass) with resulting impacts on fire behavior, but these relationships varied across study years. In particular, grazing had a stronger effect on ignition probability in drier years. Fire behavior in lightly grazed plots were similar to ungrazed plots, while moderate grazing was similar to high-intensity grazing. Results of this study highlight that grazing can be useful as a tool for wildfire management, and grazing at moderate and high intensities can reduce the probability of fire propagation in native-dominated sagebrush ecosystems. Further, the effects of grazing are context dependent and therefore may depend on specific objectives and environmental conditions.

Colosseum: Regression Test Prioritization by Delta Displacement in Test Coverage

The problem of test-case prioritization has been pursued for over three decades now and continues to be one of the active topics in software testing research. In this paper, we focus on a code-coverage based regression test-prioritization solution ( <inline-formula><tex-math notation="LaTeX">${{\sf Colosseum}}$</tex-math></inline-formula> ) that takes into account the position of changed (delta) code elements (basic-blocks) along the loop-free straight-line execution path of the regression test-cases. We propose a heuristic that logically associates each of these paths with three parameters: (i) the offset (displacement <i>a</i> ) of the first delta from the starting basic-block, (ii) the offset (displacement <i>c</i> ) of the last delta from the terminating basic block, and (iii) the average scattering (displacement <i>b</i> ) within all the intermediate basic-blocks. We hypothesize that a regression test-case path with a shorter overall displacement has a good chance of propagating the affects of the code-changes to the observable outputs in the program. <inline-formula><tex-math notation="LaTeX">${{\sf Colosseum}}$</tex-math></inline-formula> prioritizes test-cases with smaller overall displacements and executes them early in the regression test-execution cycle. The underlying intuition is that the probability of a test-case revealing a regression fault depends on the probability of the corresponding change propagation. The change in this context can potentially lead to an error. Extending this logic, delta displacement provides an approximation to failed error propagation. Evaluation on 20 open-source C projects from the Software-artifact Infrastructure Repository and GitHub (totaling: 694,512 SLOC, 280 versions, and 69,305 test-cases) against four state-of-the-art prioritizations reveals that: <inline-formula><tex-math notation="LaTeX">${{\sf Colosseum}}$</tex-math></inline-formula> outperforms the competitors with an overall 84.61% success in terms of 13 prioritization effectiveness metrics, majority of which prefer to execute top- <inline-formula><tex-math notation="LaTeX">$k\%$</tex-math></inline-formula> prioritized test-cases.

Debris flow susceptibility assessment of Leh Valley, Ladakh, based on concepts of connectivity, propagation and evidence-based probability

The Leh Valley which lies within the Trans Himalayan state of Ladakh, India, is known to be affected almost annually by debris flows ranging from minor to catastrophic scale events. The effect has been getting magnified due to increased urbanization and rapid growth in tourism industry. Though these flows are triggered by intense and abnormal rainfall events the conditioning factor has always been the topography and sediment availability. A lucid acknowledgement of the terrain condition and the degree of vulnerability of such events is required. For this a detail investigation of sediment availability, topographic conditions and their relation with known events becomes crucial. This study utilizes index of connectivity (IC) model to understand the sediment source-sink relationship and farther applied Flow-R model to simulate the probable scenario of events through predefined algorithms. We then use the Weights of evidence (WOE) method to compute the statistical probability of debris flow occurrence. This paper demonstrates the application of these three independent techniques and their implementation in a highly rugged terrain of Ladakh which is a region of frequent debris flows onslaught. The IC and Flow-R models are found to be counter supportive and effective in delineating areas which could be affected by flows that will solely originate in upstream areas where high angle channels directly connected to sediment sources are present. WOE-based model determines the probability of the rare and extensive flows that results from downward integration of other drainage networks in an open fan area.

Study on the propagation probability characteristics and prediction model of meteorological drought to hydrological drought in basin based on copula function

Studying the probability characteristics of meteorological drought transmission to hydrological drought can help alleviate drought and optimize the allocation of water resources. This study takes the Yiluo River as the research object, and Standardized Precipitation Index (SPI) and Standardized Streamflow Index (SSI) were used to represent meteorological and hydrological drought, respectively. First, the stability of the precipitation and stream flow sequence is tested by using the heuristic segmentation algorithm. The correlation between meteorological and hydrological watershed was analyzed using cross wavelet transform and lag correlation to study the transmission time of meteorological drought to hydrological drought. Second, the characteristics of drought such as drought duration, drought intensity, and drought intensity are discussed by using the run theory. Finally, the joint distribution of the SPI and SSI sequence is constructed using Copula function. From the perspective of conditional probability, the propagation probability and propagation threshold from different levels of meteorological drought to hydrological drought are calculated. A hydrological drought prediction model based on the SPI—P(SSI|SPI) relation curve is proposed. The model is also validated based on the historical data. The results show that the model is suitable for light and middle hydrological drought prediction.

An improved probability propagation algorithm for density peak clustering based on natural nearest neighborhood

Clustering by fast search and find of density peaks (DPC) (Since, 2014) has been proven to be a promising clustering approach that efficiently discovers the centers of clusters by finding the density peaks. The accuracy of DPC depends on the cutoff distance (dc), the cluster number (k) and the selection of the centers of clusters. Moreover, the final allocation strategy is sensitive and has poor fault tolerance. The shortcomings above make the algorithm sensitive to parameters and only applicable for some specific datasets. To overcome the limitations of DPC, this paper presents an improved probability propagation algorithm for density peak clustering based on the natural nearest neighborhood (DPC-PPNNN). By introducing the idea of natural nearest neighborhood and probability propagation, DPC-PPNNN realizes the nonparametric clustering process and makes the algorithm applicable for more complex datasets. In experiments on several datasets, DPC-PPNNN is shown to outperform DPC, K-means and DBSCAN.

The water assisted vinylene mechanism for cobalt Fischer-Tropsch synthesis assessed by multi-catalyst modelling of kinetics and deactivation

The paper describes development of a mechanism and a consistent rate expression for Fischer-Tropsch (FT) synthesis over cobalt-based catalysts. The developed mechanism relies on a two-step hydrogen assisted activation of CO. The carbon atom of CO is first hydrogenated by surface hydrogen to formyl; followed by the rate-limiting step whereby the oxygen atom is hydrogenated by adsorbed water. The produced CH* monomer is incorporated into the growing chain giving vinylene intermediate. The vinylene intermediate is either terminated to an olefin by adding hydrogen to the α-carbon atom or propagates by adding hydrogen to the β-carbon position. The resulting expression for CO consumption, the Fischer-Tropsch rate, can respond positively or negatively to the partial pressure of water, in agreement with experimental observations. A special feature is that the chain propagation probability does not depend on the partial pressure of hydrogen. The resulting kinetic model is tested on several cobalt catalysts supported on alumina; spanning from γ-alumina with average pore sizes ranging from 6.1 to 18.3 nm to α-alumina with a wide pore structure; and with cobalt particle sizes from 8 to 19 nm. Water was added sequentially to the syngas feed, causing enhanced deactivation, for testing the water response on activity and selectivity. A deactivation model comprising sintering and cobalt oxidation, and the FT-kinetics, describe the observed CO conversions with great precision for all catalysts. Selectivities are also well described, but with slight deviations at least partly due the effect of deactivation. Trends in some of the kinetic parameters are rationalized in terms of cobalt crystallite and pore sizes.

Research on Resource Allocation Method of Integrated Avionics System considering Fault Propagation Risk

The integrated modular avionics (IMA) system has coupled cross-linked support characteristics between physical resource entities and logical functions, and the existing resource allocation methods, which mainly consider system performance and resource utilization, do not consider the associated impact of fault propagation among resource entities. Aiming at the fault propagation risk in the resource allocation process of the IMA system, a hierarchical model of the IMA system is established. The fault propagation behavior caused by coupling association during the physical realization of the function layer logical architecture is analyzed, and the impact of different resource allocation methods on the fault propagation behavior is determined. Secondly, resource capacity constraints are established according to the resource requirement for hosted function, and the fault propagation risk model of the IMA system is constructed by considering the fault propagation impact factor, the relative importance of nodes, and the function safety criticality. The resource allocation method is evaluated according to the fault propagation risk model, and a heuristic algorithm is applied to optimize the resource allocation method of the IMA system. The simulation results show that the average propagation probability of the optimized resource allocation scheme decreases by 17.4%, and the overall fault propagation risk of the resource network decreases by 50.3%, indicating that the proposed resource allocation method can effectively improve the safety of the IMA system.

Probabilistic analysis and simulation of crack propagation in concrete pavements and surfaces

The surface of concrete pavement is susceptible to cracking. The propagation of a crack in a concrete structure may take the form of a one-dimensional line crack, a two-dimensional surface crack, or a three-dimensional volume crack. Predicting crack propagation in a concrete pavement surface is a complicated task. The whole process of formation, propagation, and orientation of cracks can be considered a stochastic process that necessitates a probabilistic investigation approach. In the present study, crack propagation in concrete pavements is studied from a probabilistic point of view using the concepts of multinomial Markov Chains using Random Walks. The study is based on actual probabilities of propagation and orientation of cracks obtained by tracing crack paths from a large dataset of images using custom-built software. Two random walker models are developed using the trinomial and multinomial Markov Chains. The master equation is developed assuming the trinomial Markov Chain, which has been brought for further theoretical and numerical developments. Examples of inferences and numerical simulations are presented to showcase the potential uses and applications of the proposed probabilistic approach for the design and scheduling of inspection visits and maintenance/rehabilitation strategies of concrete pavements and surfaces.

Risk propagation and evolution analysis of multi-level handlings at automated terminals based on double-layer dynamic network model

The propagation and evolution of risks in the system adversely affects the safety of automated terminal handlings. Therefore, it is necessary to explore the formation and development process of handling risks, predict the path and pattern of risk propagation and summarize the evolution features to improve the efficiency of multi-level handlings at automated terminals (ATMH). This paper proposes a double-layer dynamics risk propagation and evolution model. First, a complex network of risk relationships is established, and the Gillespie algorithm is employed to simulate the risk propagation dynamics based on the metric of state transition value and the dynamic mean-field theory. Next, an evolution hypernetwork is constructed and the average smooth hyperdegree distribution is derived using Poisson process and continuity methods. The simulation results show that the risk propagation link group obtained by the empirical testing is credible. The theoretical predictions of risk evolution agree well with the numerical simulations. The greater the probability of risk propagation, the more active the evolution. The model provides a certain theoretical reference for the risk management of automated terminals.

The impact of social resource allocation on epidemic transmission in complex networks

This paper focuses on the social biological communication process on double-layer complex networks. We propose a virus resource asymmetric coupling propagation model to simulate the propagation process of virus affected by recovered resources, and use a generalized discrete Markov chain method to describe the propagation dynamics. This paper mainly considers the effects of initial seed fraction, network edge coincidence degree, degree distribution heterogeneity and public resource allocation ratio on virus transmission mechanism. We find that by increasing the edge coincidence rate between nodes in the two-tier network, the propagation range of the virus changes from discontinuous growth to continuous growth with the growth of propagation probability. When the heterogeneity of network degree distribution is weak and the initial seed fraction is small, with the increase of the proportion of public resources allocation, the critical probability of virus transmission gradually increases, and the critical probability of virus outbreak first decreases and then increases. At this time, there is a critical value for the public resource allocation proportion. When the public resource allocation proportion exceeds this value, the hybrid resource allocation strategy can be better than the neighbor resource allocation strategy. In other cases, increasing the proportion of public resources allocation will reduce the critical probability of virus transmission and increase the critical probability of virus global diffusion.

A New BAT and PageRank Algorithm for Propagation Probability in Social Networks

Social networks have increasingly become important and popular in modern times. Moreover, the influence of social networks plays a vital role in various organizations, including government organizations, academic research organizations and corporate organizations. Therefore, strategizing the optimal propagation strategy in social networks has also become more important. Increasing the precision of evaluating the propagation probability of social networks can indirectly influence the investment of cost, manpower and time for information propagation to achieve the best return. This study proposes a new algorithm, which includes a scale-free network, Barabási–Albert model, binary-addition tree (BAT) algorithm, PageRank algorithm, Personalized PageRank algorithm and a new BAT algorithm to calculate the propagation probability of social networks. The results obtained after implementing the simulation experiment of social network models show that the studied model and the proposed algorithm provide an effective method to increase the efficiency of information propagation in social networks. In this way, the maximum propagation efficiency is achieved with the minimum investment.

Bayesian probabilistic propagation of hybrid uncertainties: Estimation of response expectation function, its variable importance and bounds

Uncertainties existing in physical and engineering systems can be characterized by different kinds of mathematical models according to their respective features. However, efficient propagation of hybrid uncertainties via an expensive-to-evaluate computer simulator is still a computationally challenging task. In this contribution, estimation of response expectation function (REF), its variable importance and bounds under hybrid uncertainties in the form of precise probability models, parameterized probability-box models and interval models is investigated through a Bayesian approach. Specifically, a new method, termed “Parallel Bayesian Quadrature Optimization” (PBQO), is developed. The method starts by treating the REF estimation as a Bayesian probabilistic integration (BPI) problem with a Gaussian process (GP) prior, which in turn implies a GP posterior for the REF. Then, one acquisition function originally developed in BPI and other two in Bayesian global optimization are introduced for Bayesian experimental designs. Besides, an innovative strategy is also proposed to realize multi-point selection at each iteration. Overall, a novel advantage of PBQO is that it is capable of yielding the REF, its variable importance and bounds simultaneously via a pure single-loop procedure allowing for parallel computing. Three numerical examples are studied to demonstrate the performance of the proposed method over some existing methods.