Spreading Model Research Articles

Transmission line switching spread modelling of synchrophasor smart grid network with load uncertainty inspired by epidemic model: a study of reliability and vulnerability

ABSTRACT Monitoring and controlling of transmission lines become easier in real time with synchrophasor measurements technology. Phasor Measurement Units (PMUs) provide the time-stamped synchrophasor data of the system buses and transmission lines with a high reporting rate. In this paper, a mathematical model is implemented that allows the simulation of the characteristics of the spread of transmission line switching with load variations. In the model, the transmission lines are divided into three parts defined as susceptible, infected and recovered transmission lines inspired from epidemic model. In this paper, Reliability Test System (RTS − 1979) is divided into three zones to simulate the line switching spread behaviour with load variation for reliability and vulnerability analysis of the synchrophasor smart grid network. The anomaly exists with synchrophasor measuring devices due to cyber intrusion in communication network or failure of devices makes the system observable with the characterization of vulnerable behaviour of transmission lines.

A rapid method for computing 3-D high-resolution vegetative canopy winds in weakly complex terrain

To determine near-surface winds within and above vegetation canopies for operational environmental applications, a wind model must run at high-resolution (O(1–10 m)), in a few minutes, using limited input information, and requiring minimal computing resources (e.g., personal computers). Current research models simulate large domains at coarse resolution or small domains at fine scale, but canopy simulations can take days. Fast-modeling approaches are used to solve large complex wind fields, but they oversimplify the roughness elements’ distribution impact on momentum exchanges. To overcome these deficits, the fast-running wind model QUIC-URB (Quick Urban and Industrial Complex) was augmented with a high-resolution canopy wind solver. The wind model includes a non-local factor that describes how momentum propagates through the canopy and how sub-canopy jets appear under certain conditions. QUIC-URB was also coupled with the mesoscale WRF (Weather Research and Forecasting) model to downscale wind fields from a few kilometers to a meter. The new QUIC Canopy Model resolves 3-D wind fields over hundreds of millions of cells in less than 30 s per time step on a personal computer. It was compared to two canopy models for real quasi-homogeneous and heterogeneous canopies. An error analysis shows that the model was relatively accurate with a normalized root-mean-square error of about 0.2 m s−1 in the quasi-homogeneous canopy, and a mean absolute error of 0.3 m s−1. The new model is suitable for coupling with pollution dispersion, wildfire spread, and numerical weather prediction models over weakly complex terrain, defined here as a mildly undulating environment with gradual changes in elevation and a heterogeneous distribution of plants.

Mathematical modeling and optimal control of tuberculosis spread among smokers with case detection

<p>Tuberculosis (TB) remains one of deadly infectious diseases worldwide. Smoking habits are a significant factor that can increase TB transmission rates, as smokers are more susceptible to contracting TB than nonsmokers. Therefore, a control strategy that focused on minimizing TB transmission among smokers was essential. The control of TB transmission was evaluated based on the case detection rate. Undetected TB cases often resulted from economic challenges, low awareness, negative stigma toward TB patients, and health system delay (HSD). In this study, we developed a mathematical model that captured the dynamics of TB transmission specifically among smokers, incorporating the effects of case detection. Our innovative approach lied in the integration of smoking behavior as a key factor in TB transmission dynamics, which has been underexplored in previous models. We analyzed the existence and stability of the TB model equilibrium based on the basic reproduction number. Additionally, parameter sensitivity analysis was conducted to identify the most influential factors in the spread of the disease. Furthermore, this study investigated the effectiveness of various control strategies, including social distancing for smokers, TB screening in high-risk populations, and TB treatment in low-income communities. By employing the Pontryagin maximum principle, we solved optimal control problems to determine the most effective combination of interventions. Simulation results demonstrated that a targeted combination of control measures can effectively reduce the number of TB-infected individuals.</p>

Calibrating the parameters of the cholera epidemic spread model

A system-dynamic model of cholera spread, which takes into account different ways of infection and restrictive measures, is developed. The model allows calibration of a large number of system parameters based on data on the number of infected real observations. Three scenarios of epidemic development (blurred peak, pronounced peak, plateau and two peaks) are investigated. The developed model can be modified for other scenarios and epidemics. Its implementation does not require large computing and human resources. It can be used to pre-predict the dynamics of the epidemic, as well as to organize effective measures to prevent threats associated with a pandemic.

Dynamical analysis of a fractional order SEIT epidemic model for TB spread under influence of vaccination

Dynamical analysis of a fractional order SEIT epidemic model for TB spread under influence of vaccination

On a Stochastic Approach to Extensions of the Susceptible‐Infected‐Susceptible (SIS) Model Applied to Malaria

This work presents a stochastic model of malaria spread. We first calculated the basic reproduction number R0 of the models (ShIhRhSh‐SvIv) and (ShLhIhRhSh‐SvLvIv) in order to show that the malaria‐free equilibrium is asymptotically stable; then, we used a finite Markov chain model to describe the interactions between the different compartments of the model (SeLeIeReSe‐SaLaIaRaSa‐SvIv). We carried out numerical simulations of our results for two types of transmission zones: a zone with low malaria transmission and an endemic zone. Through these simulations, we first determined the invariant stationary distribution π∗ of the model, and then, we found that the use of the indoor residual spraying (IRS) method by regular application of insecticides is more effective for the elimination of malaria than the use of long‐acting impregnated mosquito nets (LLINs).

Digital cloning of online social networks for language-sensitive agent-based modeling of misinformation spread.

We develop a simulation framework for studying misinformation spread within online social networks that blends agent-based modeling and natural language processing techniques. While many other agent-based simulations exist in this space, questions over their fidelity and generalization to existing networks in part hinder their ability to drive policy-relevant decision making. To partially address these concerns, we create a 'digital clone' of a known misinformation sharing network by downloading social media histories for over ten thousand of its users. We parse these histories to both extract the structure of the network and model the nuanced ways in which information is shared and spread among its members. Unlike many other agent-based methods in this space, information sharing between users in our framework is sensitive to topic of discussion, user preferences, and online community dynamics. To evaluate the fidelity of our method, we seed our cloned network with a set of posts recorded in the base network and compare propagation dynamics between the two, observing reasonable agreement across the twin networks over a variety of metrics. Lastly, we explore how the cloned network may serve as a flexible, low-cost testbed for misinformation countermeasure evaluation and red teaming analysis. We hope the tools explored here augment existing efforts in the space and unlock new opportunities for misinformation countermeasure evaluation, a field that may become increasingly important to consider with the anticipated rise of misinformation campaigns fueled by generative artificial intelligence.

Mechanistic modeling of alarm signaling in seed-harvester ants.

Ant colonies demonstrate a finely tuned alarm response to potential threats, offering a uniquely manageable empirical setting for exploring adaptive information diffusion within groups. To effectively address potential dangers, a social group must swiftly communicate the threat throughout the collective while conserving energy in the event that the threat is unfounded. Through a combination of modeling, simulation, and empirical observations of alarm spread and damping patterns, we identified the behavioral rules governing this adaptive response. Experimental trials involving alarmed ant workers (Pogonomyrmex californicus) released into a tranquil group of nestmates revealed a consistent pattern of rapid alarm propagation followed by a comparatively extended decay period [1]. The experiments in [1] showed that individual ants exhibiting alarm behavior increased their movement speed, with variations in response to alarm stimuli, particularly during the peak of the reaction. We used the data in [1] to investigate whether these observed characteristics alone could account for the swift mobility increase and gradual decay of alarm excitement. Our self-propelled particle model incorporated a switch-like mechanism for ants' response to alarm signals and individual variations in the intensity of speed increased after encountering these signals. This study aligned with the established hypothesis that individual ants possess cognitive abilities to process and disseminate information, contributing to collective cognition within the colony (see [2] and the references therein). The elements examined in this research support this hypothesis by reproducing statistical features of the empirical speed distribution across various parameter values.

GRASS: Learning Spatial-Temporal Properties From Chainlike Cascade Data for Microscopic Diffusion Prediction.

Information diffusion prediction captures diffusion dynamics of online messages in social networks. Thus, it is the basis of many essential tasks such as popularity prediction and viral marketing. However, there are two thorny problems caused by the loss of spatial-temporal properties of cascade data: "position-hopping" and "branch-independency." The former means no exact propagation relationship between any two consecutive infected users. The latter indicates that not all previously infected users contribute to the prediction of the next infected user. This article proposes the GRU-like Attention Unit and Structural Spreading (GRASS) model for microscopic cascade prediction to overcome the above two problems. First, we introduce the attention mechanism into the gated recurrent unit (GRU) component to expand the restricted receptive field of the recurrent neural network (RNN)-type module, thus addressing the "position-hopping" problem. Second, the structural spreading (SS) mechanism leverages structural features to filter out related users and controls the generation of cascade hidden states, thereby solving the "branch-independency" problem. Experiments on multiple real-world datasets show that our model significantly outperforms state-of-the-art baseline models on both hits@κ and map@κ metrics. Furthermore, the visualization of latent representations by t-distributed stochastic neighbor embedding (t-SNE) indicates that our model makes different cascades more discriminative during the encoding process.

RBOTUE: Rumor Blocking Considering Outbreak Threshold and User Experience

When a rumor breaks out in online social network (OSN), it can lead to significant negative impact on human society, especially in the context of public emergencies, such as pandemic. Toward restraining rumor outbreak in OSN, one of the effective containment measures is to block influential users to minimize the spread of rumors. However, most of previous efforts ignore the imbalance between the cost and effect of rumor suppression. To fill this gap, from the perspective of public opinion crisis, a dynamic rumor spread model called PISIR model is established, which takes into account the overall popularity and individual tendency of rumors. Based on this model, two rumor blocking algorithms considering outbreak threshold and user experience, called 1-Hop and 2-Hop RBOTUE algorithms, are proposed, respectively. In the algorithms, a hyperbolic discount effect-based user experience mode is introduced as the constraint to ensure the user experience in OSN, then the blocking strategy is implemented on the selected subset of nodes to keep the rumor spread scale always below the outbreak warning line. The experimental results in two synthetic networks and four real OSNs indicate that both 1-Hop and 2-Hop RBOTUE algorithms have lower rumor infection rate and require less number of blocked nodes, which means that proposed algorithms can achieve better blocking performance with less restraining cost of rumors in mainstream social networks, and the two algorithms also have different adaptability for different OSNs.

QUALITATIVE AND STABILITY ANALYSIS WITH LYAPUNOV FUNCTION OF EMOTION PANIC SPREADING MODEL INSIGHT OF FRACTIONAL OPERATOR

In an emergency, fear can spread among crowds through one-on-one encounters, with negative societal consequences. The purpose of this research is to create a novel theoretical model of fear (panic) spread in the context of epidemiology during an emergency using the fractal fractional operator. For quantitative analysis, the system’s boundedness and positivity are checked. According to the Arzela Ascoli theorem, the model is completely continuous. As a result of the discovery of Schauder’s fixed point, it has at least one solution. The existence and uniqueness of the concerned solution have been examined using the fixed point theory technique. Numerical simulations are used to demonstrate the accuracy of the proposed techniques using a generalized form of Mittag-Leffler kernel with a fractal fractional operator. Finally, simulations are utilized to represent the spread of group emotional contagion (spontaneous spread of emotions and related behaviors) dynamically.

Dynamic analysis and optimum control of a rumor spreading model with multivariate gatekeepers

Dynamic analysis and optimum control of a rumor spreading model with multivariate gatekeepers

A simple approach for studying global asymptotic stability of a malware spreading model on WSNs

A simple approach for studying global asymptotic stability of a malware spreading model on WSNs

Rumor model on social networks contemplating self-awareness and saturated transmission rate

<p>The propagation of rumors indisputably inflicts profound negative impacts on society and individuals. This article introduces a new unaware ignorants-aware ignorants-spreaders-recovereds $ (2ISR) $ rumor spreading model that combines individual vigilance self-awareness with nonlinear spreading rate. Initially, the positivity of the system solutions and the existence of its positive invariant set are rigorously proved, and the rumor propagation threshold is solved using the next-generation matrix method. Next, a comprehensive analysis is conducted on the existence of equilibrium points of the system and the occurrence of backward bifurcation. Afterward, the stability of the system is validated at both the rumor-free equilibrium and the rumor equilibrium, employing the Jacobian matrix approach as well as the Lyapunov stability theory. To enhance the efficacy of rumor propagation management, a targeted optimal control strategy is formulated, drawing upon the Pontryagin's Maximum principle as a guiding framework. Finally, through sensitivity analyses, numerical simulations, and tests of real cases, we verify the reliability of the theoretical results and further consolidate the solid foundation of the above theoretical arguments.</p>

Uncertain spreading model of internet rumors on social media platforms

Uncertain spreading model of internet rumors on social media platforms

Quantification of firebrand generation from WUI fuels for model development: Firebrand generation rate, surface temperature and heat release rate

Wildland fires that spread into developed areas, i.e., the wildland-urban interface (WUI), have resulted in numerous disasters over the past few decades, claiming the lives of civilians and causing large-scale property destruction. In these communities, fires often ignite structures by the deposition of small burning embers (firebrand attack) which is unique to these fires and not yet well understood. This study quantifies the in-flight thermal characteristics of firebrands generated by three typical vegetative fuels, namely stone pine, eucalyptus and oak found in the WUI, inside a lab-scale wind tunnel. The effects of fuel characteristics (i.e., species, moisture content and twig diameter) and ambient condition (i.e., crosswind speed) on surface temperature, number generation rate (NGR), and heat release rate (HRRfb) were investigated. Firebrand surface temperatures, measured using ratio pyrometry, were insensitive to fuel moisture content and mean twig diameter but higher at higher crosswind speeds. Surface temperature was used to estimate the HRRfb of each firebrand based on a surface energy balance and compared to overall emissions measurements. While ratio pyrometry best captured firebrand temperatures, infrared video worked well to track firebrands and derive a NGR as a function of time. Correlations were used to organize the NGR, HRRfb, and mass generation rate (MGR) under different test conditions, and unique correlations were derived for each fuel species. Results showed that the peak NGRs and MGRs for all three fuel species were positively related to crosswind speed but negatively related to fuel moisture content and mean twig diameter. The experimental data, correlations and methods disclosed in this study are important to understand the influence of competing processes on firebrand generation, parameterize fire spread models for time-dependent generation, and improve estimates of downstream ignition potential. Experiments must later be validated against large-scale data before widespread application.

THE CLARITY OF BUSINESS MODELS IN INTEGRATED ACCOUNTING REPORTS

In this study, 664 students enrolled in accounting theory courses at both the undergraduate and postgraduate levels at the University of New England were tasked with the assignment of critically assessing the decision usefulness and understandability of 2 to 4 integrated accounting reports. A key aspect of their analysis involved a critical examination of how the accounting reports conveyed the business models employed by the respective companies. In the case of consolidated reports, students were expected to observe distinct business models for each segment of the group or identify a robust rationale if only one model was presented. A team comprising both students and faculty examined all submitted reports, coding the embedded models for their complexity and understandability. The ensuing analysis revealed three distinct clusters of models, categorized as simple, orthogonal, or spread. The decision usefulness of these models exhibited significant variation and demonstrated a clear association with their understandability. Given that South Africa is the sole country mandating integrated accounting, a distinctive expectation was formed. Reports from South African corporations were anticipated to exhibit a degree of isomorphism or clustering around a specific model to a notably greater extent than reports from other countries. However, contrary to expectations, no such clustering was identified. Furthermore, it was anticipated that students would more accurately code simple business models, given their inherent simplicity and ease of comprehension. This expectation found substantial support in the results. Finally, the study hypothesized that spread models would be more prone to being cluttered by extravagant claims, messianic declarations, and legitimation discourses compared to simple models. This hypothesis received confirmation through the study's findings.

A mathematical model of meningitis with antibiotic effects

The mathematical model in this study is a SCIR-type meningitis disease spread model, namely susceptible (S), carrier (C), infected (I), and recovery (R). In the model used, there are two equilibrium points, namely the disease-free equilibrium point and the endemic equilibrium point. The conditions and stability of the equilibrium point are determined by the basic reproduction number, which is the value that determines whether or not the spread of meningitis infection in a population. The results of this study show that the stability of the disease-free equilibrium point and the endemic equilibrium point are locally asymptotically stable and by using the Lyapunov Function method it is found that the disease-free equilibrium point will be globally stable when, while the endemic equilibrium point will be globally stable when numerical simulations perform to support the theoretical results.

WSLC: Weighted semi-local centrality to identify influential nodes in complex networks

Identifying and ranking influential nodes in complex networks is a critical aspect to study the survival and robustness of networks. Many ongoing researches have proposed centrality metrics to address this problem, so that the performance of each is attributed to specific scenarios. For example, metrics based on local structure have low ranking accuracy due to the use of limited information, and metrics based on global structure suffer from high complexity. Meanwhile, metrics based on semi-local structure are amazingly well, but an efficient centrality for identifying influential nodes is still not available due to differences in the structure and scale of networks. In addition, most semi-local centrality metrics only consider one aspect of each node's information, and their development still faces serious challenges. This paper develops a Weighted Semi-Local Centrality (WSLC) to identify influential nodes in complex networks based on extended neighborhood concept. Here, several different weights are investigated to find the best performance on WSLC. We use the extended neighborhood concept to select the nearest neighbors, which considers the global information of the network in a limited and efficient way to calculate the ranks. Here, a distributed approach is presented that can cut a subgraph of the entire network for each node with low complexity. This subgraph contains neighbors with different hops, which are used to maintain high efficiency when facing large-scale networks. In addition to the importance of the node itself, WSLC also combines the importance of the node's nearest neighbors with different hops for ranking. Therefore, defining semi-local structure with a distributed approach as well as using an efficient edge weighting policy differentiates WSLC from other existing centrality metrics. The evaluation of WSLC has been done through several real-world networks using Kendall's correlation. The effectiveness of WSLC under the SIR infection spreading model has been verified by extensive simulations compared to state-of-the-art centrality metrics.

Providing a model to evaluate the spread of fire in a chemical warehouse using numerical simulation and Bayesian network

Chemical warehouses are one of the important sources in process units. Locations are one of the issues that should be investigated in closed environments due to the many types of chemicals and storage conditions. In this study, fire dynamics simulation (FDS), was used to simulation of fire in the presence and non-presence of the ventilation system. Then the spread of fire in the warehouse was evaluated based on domino methods and finally the Bayesian network (BN) was used to update the rate of fire spread. The simulation results in this approach showed that the highest amount of heat flux related to iso-octane fire at L/D= 0.3 when the ventilation system is off and the doors are closed. The updated results of the fire spread modeling in BN showed that if a fire occurs in isooctane barrels, it has the highest rate of spread with a probability of 22% and the lowest rate of spread in Diglycolamine(DGA) barrels with a probability of 0.4%. From the results of this study, it is possible to understand the importance of arranging materials according to the principles of compatibility of chemical materials, and based on this, appropriate control measures can be provided.