Scope Of Propagation Research Articles

Mining Delay Propagation Causality within an Airport Network from Historical Data

Airport networks are interconnected through flight routes, with delays at upstream airports leading to delays at downstream airports, thus causing delay propagation. Exploring the mechanisms of delay propagation in airport networks provides scientific insights for managing and controlling delays in aviation systems. Existing methods, such as Granger causality tests and transfer entropy, must be revised to address the nonlinear causal relationships of delays in airport networks. So, this paper proposes a causality mining method for delay propagation in airport networks based on partial correlation-based multivariate conditional independence (PCMCI). This method comprehensively considers all airports and causality mining in two stages. The first stage uses conditional independence tests to obtain the parent node set of the target airport, which includes both true and false causal relationships. The second stage employs instantaneous conditional independence tests to eliminate false causal relationships and obtain test statistics representing the strength of causality. Based on historical delay data from US airports over a year, the experimental results show that multiple factors cause delay propagation in airport networks rather than a single causal relationship. The scope of delay propagation is limited, mainly affecting a few airports closely connected to it. Delays at airports with small flight volumes are more likely to propagate. Few airport pairs in the network mutually propagate delays and, often, delays at airports affected by a particular airport’s delay also exhibit causal relationships with each other. This method provides a new perspective for deepening the understanding of delay propagation mechanisms in airport networks.

Investigation of risk propagation and control in emergency response logistics networks: A cellular automata based approach

Emergency logistics is crucial for responding to natural disasters and public health emergencies. In emergency logistics systems, risk propagation must be effectively controlled and mitigated, and otherwise it may lead to serious consequences. This study, for the first time, investigates the risk propagation of emergency logistics networks and their protection strategies from a dynamic perspective using the method of cellular automata. The purpose is to reveal the risk propagation mechanism of emergency logistics networks and its influencing factors, which provides a basis for improving the risk resistance of emergency logistics networks. Specifically, this paper constructs an emergency logistics network based on the weighted scale-free algorithm and establishes a risk propagation model by using metric automata. Also, this paper carries out a comprehensive numerical investigation on risk propagation in the emergency logistics network by adjusting the model parameters and adopting different protection strategies. The results of this research indicate that (1) the speed and range of risk propagation are not sensitive to the network size, while the reduction of the network size leads to an increase in the volatility and a decrease in the stability of the propagation; (2) the state characteristics of the network nodes are more important than the structural characteristics of the network for controlling the risk of node vulnerability; (3) increasing the level of risk recovery is more effective than reducing risk infection in controlling the scope of risk propagation throughout the network; (4) finally, the targeted protection strategy is more effective than the random protection strategy. The findings are potentially impactful in that they reveal the nature of risk propagation in emergency logistics networks, further expand methodological research of risk propagation, and provide a solid foundation for effective risk management and control.

Rumor detection on social media through mining the social circles with high homogeneity

The massive spread of rumors on social media has become a major global challenge, increasing the urgent demand for rumor detection. Although social circles are ubiquitous in social networks and have the property of describing users' behavioral preferences, they have not been explicitly considered in rumor detection models. Meanwhile, information diffusion studies have shown that social circles have a significant impact on the speed, scope, and content of rumor propagation. Motivated by this important absence, we validate the significant difference between the social circles of rumor and non-rumor sources, and propose a new rumor detection algorithm. The algorithm explores a new feature space by extracting social circles with high homogeneity from user context, and combines it with social interaction to automatically detect rumors. Experimental results obtained on three real-world datasets support that the proposed approach outperforms state-of-the-art methods and displays a superior capacity for detecting rumors at early stages. The code of this work is made publicly available to foster any further research.1

Finding spatial and temporal features of delay propagation via multi-layer networks

Air traffic system is a typical complex network with dynamic delay propagation between airports. However, difficulties in measuring delay propagation strength and constructing rational networks make investigating the features of delay propagation based on real data from the perspective of complex network is rarely seen. In this paper, using two largest real-world flights dataset of China and the USA over a period of two months in 2018 (from Jul. to Aug.), we construct the delay propagation networks among airports by calculating causality relationship between delay time series of airports. We identify the multilayer structures of networks by k-core decomposition and reveal that a core layer with a few of tightly connected airports dominants delay propagation in the whole systems. Through the analysis on motifs, bidirectional edges are found to be the culprit to expand the scope of delay propagation from airport pairs to local structures. At the system level, we analyze the properties of communities and found that airports geographically close to each other are likely to be classified into a delay propagation community, while the delay propagation between communities is mainly affected by the air traffic flow between them. Moreover, through the comparisons of structures for the delay propagation networks on different days, we reveal the temporal dependence of delay propagation that the more serious the delay propagation in one day, the more similar it will be to that in the next day. Finally, the usefulness of features found is assessed through applying them to predict the delay propagation in the future.

The impact of disclosure of risk information on risk propagation in the industrial symbiosis network.

The interdependent symbiotic relationship between enterprises may bring potential risks to the stability of the industrial symbiosis network (ISN). In order to reduce the damage caused by further risk propagation to the system, this paper establishes the multiplex network to study the impact of disclosure of risk information on risk propagation. In the multiplex network, we use a small-world network to simulate a social network and propose an evolutionary model with scale-free characteristics to simulate the symbiotic relationships between enterprises. Then we establish a risk propagation model by defining transition rules among various states. Through theoretical analysis using the Microscopic Markov Chain Approach (MMCA), we find that the proportion of disclosed enterprises, the network structure of the ISN, the recovery rate of enterprises, and the degree of symbiotic dependence affect the risk propagation threshold of the ISN. Numerical simulation results show that increasing the disclosure probability of risk information can reduce the scope of risk propagation. Moreover, once the disclosure probability of risk information reaches a certain value, the risk propagation threshold can be increased. Finally, relevant suggestions are put forward: (i) strengthening the information communication between symbiotic enterprises may reduce risks caused by information asymmetry. (ii) In addition to the authenticity and integrity of risk information, it is necessary to prevent risk information from being over-interpreted or exaggerated. (iii) Enterprises should strengthen the ability to recover from risks, appropriately reduce the degree of symbiotic dependence, and enhance risk awareness to reduce the possibility of risk occurrence.

TriTF: A Triplet Transformer Framework Based on Parents and Brother Attention for Hyperspectral Image Change Detection

Hyperspectral image (HSI) change detection (CD) is a technique to accurately detect land cover changes by using HSIs with rich spatial-spectral information. In recent years, the HSI-CD methods based on convolutional neural networks (CNNs) have achieved great success because of their flexible and effective feature extraction ability. However, these methods often take the HSI patches as the input of the networks, which undoubtedly hinders the overall perception of the HSIs. Meanwhile, the valuable temporal information in HSIs is often underutilized. For this end, a triplet transformer framework (TriTF) based on parents-temporal attention and brother-spatial attention is proposed for HSI-CD. The proposed framework mainly contains the following three parts: 1) Transformer-based network backbone, which uses the self-attention to capture the correlation between arbitrarily two pixels in the same patch and extracts the global spatial correlation in the unit of encoded input patches; 2) parents-temporal attention (PTA) branch. Unlike the previous cross-temporal attention mechanisms of the “T1↔T2” mode which only consider the interaction between bi-temporal HSIs, this paper constructs a novel PTA of the “T1→T3←T2” mode which takes the difference-temporal image T3 as the core. The impact of bi-temporal HSIs on the land cover changes is more concerned in the PTA; 3) brother-spatial attention (BSA) branch. The most similar patch in the current training batch of each patch is defined as its brother patch. Furthermore, cross-spatial attention is applied to propagate the features of the brother patch to the current patch. Thus, the middle- and long-range dependencies can be utilized and the scope of feature propagation can be extended. In this paper, the experiments under low and high sampling rates are conducted and proved the outstanding change detection performance of the proposed TriTF when compared with abundant state-of-the-art (SOTA) CD algorithms. The source code of this paper will be released at https://github.com/zkylnnu/TriTF.

Disaster propagation in interdependent networks with different link patterns.

Disaster propagation in complex, interdependent, and multilayered networks has attracted considerable research interest in recent years. In this paper, we propose a model that combines two dynamic mechanisms, i.e., the spreading of failure in layer-dependent networks, where each node in a layer depends on one in another layer. We first investigate the robustness of the Erdős-Rényi (ER)-ER, scale-free (sf)-ER, and sf-sf pattern of interdependent networks against cascading failure with different probabilities of triggering, and then use the random link, assortative link, and disassortative link patterns between the networks to analyze the scope of propagation of failure. The numerical results show that with increasing probability of triggering, the number of damaged nodes in both layers increased and the robustness of the scale-free network to random failures decreased due to the interdependence. Regardless of the topological structure, the two layers eventually tended to have similar failure characteristics due to their interdependence. In addition, the different link patterns had a significant effect on enhancing disaster propagation in interdependent networks.

Element activation method and non-conformal dynamic remeshing strategy to model additive manufacturing

Modeling of Additive Manufacturing (AM) at the part scale involves non-linear thermo-mechanical simulations. Such a process also imposes a very fine discretization and requires altering the geometry of the models during the simulations to model the addition of matter, which is a computational challenge by itself. The first focus of this work is the addition of an additive manufacturing module in the fully implicit in-house Finite Element code Metafor [1] which is developed at the University of Liège. The implemented method to activate elements and to activate and deactivate boundary conditions during a simulation is adapted from the element deletion algorithm implemented in Metafor in the scope of crack propagation [2]. This algorithm is modified to allow the activation of elements based on a user-specified criterion (e.g. geometrical criterion, thermal criterion, etc.). The second objective of this work is to improve the efficiency of the AM simulations, in particular by using a dynamic remeshing strategy to reduce the computational cost of the simulations. This remeshing is done using non-conformal meshes, where hanging nodes are handled via the use of Lagrange multiplier constraints. The mesh data transfer used after remeshing is based on projection methods involving finite volumes [3]. The presented model is then compared against a 2D numerical simulation of Direct Energy Deposition of a High-Speed Steel thick deposit from the literature [4].

Spectator‐filter‐spreader‐stifler information propagation model with wake‐up mechanism in social networks

SummaryNowadays, social network platforms play a significant role in daily life, allowing people to hear what is going on around the world without leaving home. The research on the internal transformation of information propagation is critical, which can help restrain the propagation of fake information or rumors and promote the widespread dissemination of effective information. To better understand the transition of states in information propagation, this article considers a new filter state that does not transmit information temporarily and establishes a spectator‐filter‐spreader‐stifler (SFIR) model. Mainly, to explore the dynamics of information propagation, the article introduces two different wake‐up mechanisms including the spontaneous awakening of the filter and the infection of the leader spreaders to the passive stifler, which relies on the proportion of current spreaders as well as the doubt degree on information. Afterward, based on the average field equations built, the nonpropagation equilibrium point E0 and propagation equilibrium point E1 are obtained by theoretical analysis. Moreover, the simulation results reveal that spontaneous wake‐up mechanism accelerates the information propagation while leader spreaders infection increases the scope of information propagation, which can provide theoretical support for controlling information propagation more effectively.

Influence maximization based on activity degree in mobile social networks

SummaryThe problem of influence maximization (IM) has become an important research topic due to the rapid growth of mobile social networks. It attempts to identify a set of nodes, referred to as influencers, contributing to the spread of maximum information. In this article, we present the construction of social relation graph based on mobile communication data. And we propose a new centrality measure—activity degree to characterize the activity of nodes. By combining the local attributes of nodes and the behavioral characteristics of nodes to measure node activity degree, which can be used to evaluate the influence of users in mobile social networks, we introduce Susceptible‐Infected‐Susceptible model to simulate the dynamic spreading of information. We take advantage of the two indicators the degree centrality and the betweenness centrality to get a better ranking results. In comparison with spanning graph and initial graph, the results of comparison demonstrate that our algorithm has advantages in the scope of influence propagation.

Integrated detection of disruption scenarios, the ripple effect dispersal and recovery paths in supply chains

The studies on supply chain (SC) disruption management frequently assume the existence of some negative scenarios and suggest ways to proactively protect and reactively recover the SC operations and performance if such scenarios occur. Though, there is a paucity of research on how to support methodologically the detection of realistic disruption scenarios, ideally of different risk aversion degrees. The contribution of our study lies in a conceptualization of a new methodical approach to the detection of disruption scenarios, ripple effect dispersal and recovery paths in supply chains on the basis of structural genomes. The objective is to integrate and expand the existing knowledge gained isolated in robustness analysis and recovery planning into a comprehensive framework for building a theory as well as for managerial purposes. The outcomes of this research constitute a useful decision-making support tool that allows detecting disruption scenarios at different risk-aversion levels based on the quantification of the structural robustness with the use of the genome method and observing the scope of disruption propagation, i.e., the ripple effect. The advantage of using a robustness computation by the genome method is that this allows detecting both the disruption scenarios of different severity, the ripple effect dispersal, and the corresponding recovery paths. Our results can be of value for decision-makers to compare different supply chain structural designs regarding the robustness and to identify disruption scenarios that interrupt the supply chain operations to different extents. The scenario detection can be further used for identifying optimal reconfiguration paths to deploy proactive contingency and reactive recovery policies. We show a correlation between the risk aversion degree of disruption scenarios and the outcomes of the reconfiguration policies.

Evolutionary Game Dynamics of the Competitive Information Propagation on Social Networks

To better control the scope of information propagation and understand its dynamic characteristics, we propose an information propagation model based on evolutionary game theory. The model can simulate an individual’s strategy selection in social networks when facing two pieces of competitive information, whereby “competitive information” is defined as two pieces of information which have the opposite meaning. First, a reasonable payoff function is designed for individuals based on pairwise interaction. Second, each individual selects a friend it trusts. Third, a probability value is used to indicate whether an individual imitates the strategy of the selected friend. In the model, we consider not only the heterogeneous influence of friends’ strategies on individual decision‐making in the process of communication but also the attenuation of individuals’ attention to information when information about friends is received repeatedly. The simulation results show that our model can accurately simulate the propagation of two pieces of competitive information. Furthermore, we find that the basic payoff that accrues to individuals as a result of spreading their information and the network topology are two factors that significantly influence the propagation result. The results provide effective insights into how to better control and guide public opinion.

Mean field dynamics of a Wilson–Cowan neuronal network with nonlinear coupling term

In this paper we prove the propagation of chaos property for an ensemble of interacting neurons subject to independent Brownian noise. The propagation of chaos property means that in the large network size limit, the neurons behave as if they are probabilistically independent. The model for the internal dynamics of the neurons is taken to be that of Wilson and Cowan, and we consider there to be multiple different populations. The synaptic connections are modeled with a nonlinear “electrical” model. The nonlinearity of the synaptic connections means that our model lies outside the scope of classical propagation of chaos results. We obtain the propagation of chaos result by taking advantage of the fact that the mean-field equations are Gaussian, which allows us to use Borell’s Inequality to prove that its tails decay exponentially.

Response of guava (Psidium guajava L.) softwood cuttings to paclobutrazol application in different rooting media

The scope of clonal propagation of guava (Psidium guajava L.) by soft wood cuttings collected from mature plant is described. The most successful media among those traditionally used and to identify promising alternatives was determined. Soft wood cuttings were done to determine the most successful medium and Paclobutrazol (PBZ) hormone concentration. Guava soft wood cuttings were treated with 0, 200, 400 and 600 mg/l PBZ solution and planted in three different rooting media (sand, silt and top soil) and rooted in low-tunnel. The cuttings were allowed to grow under low-tunnel for three months to assess the rooting capability and shooting competency. The study revealed that the species has a great potential for clonal propagation through soft wood cuttings.

Study on the Propagation Laws of Hydrofractures Meeting a Faulted Structure in the Coal Seam

Hydraulic fracturing is an important technique for increasing coal seam permeability and productivity of CBM (coalbed methane). As a common type of faulted structure in the coal seam, the fault has a direct impact on the direction and scope of hydrofracture propagation, weakening fracturing effects. To study the propagation laws of a hydrofracture meeting a fault in the coal seam, based on a two-dimensional model of a hydrofracture meeting a fault, the combined elastic mechanics and fracture mechanics, the propagation mode, critical internal water pressure, and influencing factors were analyzed. A numerical simulation on the propagation laws of hydrofracture meeting a fault was conducted by using the coupling system of flow and solid in the rock failure process analysis (RFPA2D-Flow). The results show that the horizontal crustal stress difference, the intersection angle between hydrofracture and fault plane, and the physical mechanics characteristics of coal-rock bed are the main factors influencing fracture propagation. With a decrease of horizontal crustal stress differences, intersection angle and an increase of roof elasticity modulus, it is easier for the footwall hydrofracture to enter the hanging wall along the bedding plane, forming an effective fracture. When the stress difference is large and the dip angle of fault plane surpasses 45°, the hydrofracture is easy to propagate towards the coal roof and floor by going through the fault plane. At this time, the coal seams of the footwall and the hanging wall should be fractured respectively to ensure fracturing effects, and the support of the roof and floor should be strengthened. The field experiment, theoretical analysis and numerical simulation were consistent in their results, which will contribute to the optimization of hydraulic fracturing and the prediction of hydrofracture in the coal seams containing faults.

Network-based analysis of software change propagation.

The object-oriented software systems frequently evolve to meet new change requirements. Understanding the characteristics of changes aids testers and system designers to improve the quality of softwares. Identifying important modules becomes a key issue in the process of evolution. In this context, a novel network-based approach is proposed to comprehensively investigate change distributions and the correlation between centrality measures and the scope of change propagation. First, software dependency networks are constructed at class level. And then, the number of times of cochanges among classes is minded from software repositories. According to the dependency relationships and the number of times of cochanges among classes, the scope of change propagation is calculated. Using Spearman rank correlation analyzes the correlation between centrality measures and the scope of change propagation. Three case studies on java open source software projects Findbugs, Hibernate, and Spring are conducted to research the characteristics of change propagation. Experimental results show that (i) change distribution is very uneven; (ii) PageRank, Degree, and CIRank are significantly correlated to the scope of change propagation. Particularly, CIRank shows higher correlation coefficient, which suggests it can be a more useful indicator for measuring the scope of change propagation of classes in object-oriented software system.

Using Fault Injection to Analyze the Scope of Error Propagation in Linux

Using Fault Injection to Analyze the Scope of Error Propagation in Linux

Using Fault Injection to Analyze the Scope of Error Propagation in Linux

Using Fault Injection to Analyze the Scope of Error Propagation in Linux

In vitro Clonal Propagation of Vitex negundo L.-An Important Medicinal Plant

Vitex negundo L., a perennial shrub belonging to the family Verbenaceae, is an important medicinal plant. It grows abundantly in St. Martin’s Island and commonly known as Nishinda. Generally leaf is used for medicinal purpose but root, flower and fruit also have the medicinal values (Hasan 1982). Leaf of the plant contains essential oil, an alkaloid, nishindin. Stem and bark contain flavonoid glycosides. Leaves of nishinda very effectively reduce the inflamma-tory swellings of joints in rheumatic attacks. Juice of fresh leaf removes fetid discharges and worms from ulcers. Flower oil is applied to sinuses and scrofulous sores. Root juice is tonic, expectorant and diuretic (Ghani 1998). Tissue culture has greatly enhanced the scope and potentiality of mass propagation by exploiting the regenerative behavior in a wide range of selected horticultural and agricultural plants including the medicinal ones (Roy et al. 1994, Thiruvengadam and Jayabalan 2000, Islam et al. 2001 and Jawahar et al. 2008). Keeping in mind the medicinal importance of all the parts of this plant, an attempt has been made to formulate its regeneration system through in vitro culture. Key words: Vitex negundo, medicinal plant, Clonal propagation D.O.I. 10.3329/ptcb.v19i1.5005 Plant Tissue Cult. & Biotech. 19(1): 113-117, 2009 (June) –Short Communication

Flight Delay Propagation Impact on Strategic Air Traffic Flow Management

Despite recent decreases in air traffic, congestion is still at higher levels than it was several years ago. Because of this congestion and expected increases in traffic, strategic planning for daily operations in the air traffic system has taken on an increasingly important role. Weather typically has a strong influence in developing such plans, as it leads to degraded airport and airspace capacity, which in turn causes delays. However, many reported delays are induced by these initial effects. Thus, it is essential to understand the scope and impact of propagation of flight delays in a strategic setting. In this paper, delay propagation is examined over several years in spatial and temporal terms. Two models, each incorporating different levels of fidelity and flexibility, are applied in an effort to examine this phenomenon. The first model tracks individual aircraft as they operate several flights and explicitly separates the new delay from the propagated delay. The second model operates at a higher level by relating earlier and later delays at several airports. Results identifying the various impacts of delay propagation are reported for several airports and scenarios, and recommendations are made for incorporating these models and their output into strategic planning efforts.