Cascade Model Research Articles

Threshold Cascade Dynamics in Coevolving Networks.

We study the coevolutionary dynamics of network topology and social complex contagion using a threshold cascade model. Our coevolving threshold model incorporates two mechanisms: the threshold mechanism for the spreading of a minority state such as a new opinion, idea, or innovation and the network plasticity, implemented as the rewiring of links to cut the connections between nodes in different states. Using numerical simulations and a mean-field theoretical analysis, we demonstrate that the coevolutionary dynamics can significantly affect the cascade dynamics. The domain of parameters, i.e., the threshold and mean degree, for which global cascades occur shrinks with an increasing network plasticity, indicating that the rewiring process suppresses the onset of global cascades. We also found that during evolution, non-adopting nodes form denser connections, resulting in a wider degree distribution and a non-monotonous dependence of cascades sizes on plasticity.

Marginal Scale State Analysis of Cloud Computing Economy Based on a Class of First Order Differential Equations

Abstract This paper studies the internal effects of cloud technology on the economic development of our country based on a primary differential equation. As the main production factor, cloud technology has expanded enterprises’ economic growth. The first is to separate cloud computing from physical production as an emerging mode. In the framework of “innovation destruction,” the end biochemical technology in the cloud is introduced into the product function, and the mass cascade model of the first-order differential equation is established. It can be seen from the theoretical deduction that cloud computing plays a vital role in improving the “multiplier effect” of product quality and leading to the “transformation of research and development mode.” Secondly, the way and mechanism of cloud computing to promote technology development and economic development are studied, and numerical simulation is carried out. The results show that “convergence costs” between cloud computing and other manufacturing factors cause “R&D shifts.” This harms the short-term economic output of enterprises.

Cascading Robustness Analysis of Wireless Sensor Networks with Varying Multisink Placement.

In practical wireless sensor networks (WSNs), cascading failures are closely related to network load distribution, which in turn strongly relies on the locations of multiple sink nodes. For such a network, understanding how the multisink placement affects its cascading robustness is essential but still largely missing in the field of complex networks. To this end, this paper puts forward an actual cascading model for WSNs based on the multisink-oriented load distribution characteristics, in which two load redistribution mechanisms (i.e., global routing and local routing) are designed to imitate the most commonly used routing schemes. On this basis, a number of topological parameters are considered to quantify the sinks' locations, and then, the relationship between these quantities with network robustness is investigated on two typical WSN topologies. Moreover, by employing the simulated annealing approach, we find the optimal multisink placement for maximizing network robustness and compare the topological quantities before and after the optimization to validate our findings. The results indicate that for the sake of enhancing the cascading robustness of a WSN, it is better to place its sinks as hubs and decentralize these sinks, which is independent of network structure and routing scheme.

PIGNUS: A Deep Learning model for IDS in industrial internet-of-things

The heterogeneous nature of the Industrial Internet of Thing (IIoT) has a considerable impact on the development of an effective Intrusion Detection System (IDS). The proliferation of linked devices results in multiple inputs from industrial sensors. IDS faces challenges in analyzing the features of the traffic and identifying anonymous behavior. Due to the unavailability of a comprehensive feature mapping method, the present IDS solutions are non-usable to identify zero-day vulnerabilities.In this paper, we introduce the first comprehensive IDS framework that combines an efficient feature-mapping technique and cascading model to solve the above-mentioned problems. We call our proposed solution deeP learnIG model intrusioN detection in indUStrial internet-of things (PIGNUS). PIGNUS integrates Auto Encoders (AE) to select optimal features and Cascade Forward Back Propagation Neural Network (CFBPNN) for classification and attack detection. The cascading model uses interconnected links from the initial layer to the output layer and determines the normal and abnormal behavior patterns and produces a perfect classification. We execute a set of experiments on five popular IIoT datasets: gas pipeline, water storage tank, NSLKDD+, UNSW-NB15, and X-IIoTID. We compare PIGNUS to the state-of-the-art models in terms of accuracy, False Positive Ratio (FPR), precision, and recall. The results show that PIGNUS provides more than 95% accuracy, which is 25% better on average than the existing models. In the other parameters, PIGNUS shows 20% improved FPR, 10% better recall, and 10% better in precision. Overall, PIGNUS proves its efficiency as an IDS solution for IIoTs. Thus, PIGNUS is an efficient solution for IIoTs.

Positive Evaluation Maximization in Social Networks: Model and Algorithm

Influence maximization (IM) is a classical and heated issue in online social networks. Although some works have studied multifeature IM, they do not consider this problem from the perspective of users&#x2019; preferences. In this article, we construct a novel multifeature spreading model that considers users&#x2019; different preferences, namely, the MFP-independent cascade (IC) model, which uses the IC model as the basic propagation model. Some features are positive or negative depending on users&#x2019; preferences. According to this spreading model, we consider a novel problem that focuses on the influence gap between positive features and negative features, namely, positive evaluation maximization (PEM). This problem is NP-hard and nonsubmodular. Fortunately, PEM can be expressed as DS decomposition because the positive influence and the negative influence are both monotone submodular. Based on DS decomposition, we design some special greedy strategies, namely, the parametric conditioned greedy (PCG). To reduce the computational cost of our method, we design fast PCG (FPCG) algorithm using the sampling technique. In addition, the approximation ratio of the PCG and FPCG approaches is only the gap between their <inline-formula> <tex-math notation="LaTeX">$O(\epsilon)$</tex-math> </inline-formula> values. Finally, we evaluate our algorithms by performing massive experiments on real datasets.

EPH65 The Analysis of Dynamic Processes of Virus Transmission: How Could Diagnostic Tests and Vaccinations Can Stop COVID-19 Pandemic?

COVID-19 reached its fourth year of pandemic since 2020. The repeated waves of infections have been driven by multiple factors such as pathological traits of variants, diagnostic accuracy, and vaccination conditions. This study revisits and analyzes the dynamic processes of viral transmission to generate new scientific knowledge. A cascade model of viral transmission from one case to another was developed, and theoretically analyzed how the number of infected cases at time t, D＋[t], can be changed at time t+1, D＋[t+1], considering six parameters: 1) k:level of transmission, 2) Rt: effective reproduction number, 3) ρ: capture rate of infected cases, 4) θ: immunity protection rate in individuals, 5) ε: evasion rate from vaccines, and 6) Sn: test sensitivity. The formula which associates D＋[t] with D＋[t+1] was given as follows: D＋[t+1] = K・D＋[t], where K = {(1－Sn) + (1－ρ) / ρ}{1－Rtk (1–θ(1-ε))k} / {1－Rt (1–θ(1-ε))}. Also, assuming K be smaller than 1, the lower limit of test sensitivity to stop the viral transmission was formulated: Sn > {Rt (1–θ(1-ε))－Rtk(1–θ(1-ε))k} / {(1－Rtk(1–θ(1-ε))k)ρ}. In example computations, the formula indicated that a one-off PCR test with the sensitivity of 85% would not be sufficient to contain highly contagious infections such as the Omicron variants, and that it would be practically impossible to control the situation with the immune-evasive sub-variants in circulation. The theory developed in this study broadens the science on evidence-based public health and will be useful for outcomes studies and informed decisions on public policy for pandemic control.

Robustness of community networks against cascading failures with heterogeneous redistribution strategies

Network robustness is one of the core contents of complex network security research. This paper focuses on the robustness of community networks with respect to cascading failures, considering the nodes influence and community heterogeneity. A novel node influence ranking method, community-based Clustering–LeaderRank (CCL) algorithm, is first proposed to identify influential nodes in community networks. Simulation results show that the CCL method can effectively identify the influence of nodes. Based on node influence, a new cascading failure model with heterogeneous redistribution strategy is proposed to describe and analyze node fault propagation in community networks. Analytical and numerical simulation results on cascading failure show that the community attribute has an important influence on the cascading failure process. The network robustness against cascading failures increases when the load is more distributed to neighbors of the same community instead of different communities. When the initial load distribution and the load redistribution strategy based on the node influence are the same, the network shows better robustness against node failure.

Electric vehicle charging path planning considering couped faults in distribution-transportation network

Once a distribution network failure occurs, it can spread to the traffic network through the coupling point, causing electric vehicles (EVs) to change their charging paths. To address this problem, this paper presents an EV charging path planning approach that considers coupled faults in the distribution-transportation network. First, the cascading failure model of the distribution-transportation network and the model for choosing charging stations are presented to transfer the information of coupling faults propagation and coupling points power interaction to the follow-up path planning scheme. Second, a time occupancy road resistance model that considers congested and unobstructed traffic states is proposed to calculate the road section travel time, based on the analysis results of the evolution process of road traffic flow queuing using traffic wave theory. For the speed and density parameters in the traffic wave model, values are calculated using the logistics speed-density model and the time occupancy model. Third, a multi-objective optimization function that integrates travel cost and coupling network operation state is determined from the perspective of hindering the propagation of coupling faults. The function is solved to recommend optimal charging paths using an improved A* searching algorithm. Finally, a 90-bus road network and three 33-bus distribution networks are selected as examples to verify the veracity and validity of the proposed model and method. The research results demonstrate that the proposed method can alleviate traffic congestion.

Fuzzy sign-aware diffusion models for influence maximization in signed social networks

The diffusion models in the influence maximization problem are a trending topic in many companies' viral marketing to raise their business promotion. The majority of existing diffusion models have focused only on trust relationships, and a few models have considered distrust relationships. Nevertheless, the latter models lack appropriate theoretical support to cover social influences resulting from different user-relationship types and still do not provide proper predictions. In this study, a fuzzy-based approach is first introduced to model the influence propagation for different user-relationship types. Then, four novel fuzzy sign-aware diffusion models named FSC-SB, FSC-N, FST-SB, and FST-N are proposed by the introduced fuzzy-based approach in two categories: cascade and threshold-based models. In the proposed models, the user-relationship type is determined by a fuzzy expert system in which a natural multi-trust level relationship is applied instead of a commonly used crisp relationship. Moreover, new rules and equations are defined to determine a user's state by information received from its active neighbors. The performance of proposed models was compared with some state-of-the-art models conducted by two real-world networks, Bitcoin OTC and Bitcoin Alpha. The experimental results showed that the proposed models enhance the prediction accuracy and make effective decisions in viral marketing.

Gas/Liquid Operations in the Taylor-Couette Disc Contactor: Continuous Chemisorption of CO2

Gas/liquid contactors are widely used in chemical and biotechnological applications. The selection and design of bubble-column-type gas/liquid contactors requires knowledge about the gas distributor design to provide appropriate gas flow patterns. This study presents the continuous chemisorption of CO2 in 0.1 molar sodium hydroxide solution in a counter currently operated gas/liquid Taylor-Couette disc contactor (TCDC). This vertical-column-type contactor is a multi-stage agitated gas/liquid contactor. The performance of a lab-size TCDC contactor in gas/liquid mass transfer operations was investigated. The apparatus design was adjusted for gas/liquid operations by installing perforated rotor discs to provide a rotational-speed-dependent dispersed gas phase holdup in the column. The parameters of dispersed gas phase holdup, volumetric mass transfer coefficient and residence time distribution were measured. In the first step, hydraulic characterization was performed. Then, the efficiency in gas/liquid operations was investigated by continuous neutralization of 0.1 molar sodium hydroxide with a gas mixture of 30 vol% CO2 and 70 vol% N2. Temperature, rotational speed and gas flow rate were varied. The desired pH value of pH 9 at the column outlet was kept constant by adjusting the sodium hydroxide feed. From the experimental results, the volume-based liquid-side mass transfer coefficient kLa was deduced in order to model the reaction according to the two-film theory over the column height. The CSTR cascade model fitted the experimental data best. The experimental results confirm stable and efficient reactive gas/liquid contact in the Taylor-Couette disc contactor.

Causality Extraction Cascade Model Based on Dual Labeling

Causal relation extraction is a crucial task in natural language processing. Current extraction methods have problems, including low accuracy of causal-event division and incorrect extraction of important semantic features. This study uses the bidirectional long short-term memory (BiLSTM) and attentive convolutional neural network (ACNN) models to construct a cascaded causal relationship extraction model to improve the precision of the extraction. The model uses two kinds of labels and then divides the causal event boundary after determining the relationship between the front and rear causal events. It automatically learns semantic features from sentences, reducing the dependence on external knowledge and improving the precision of extraction. The experimental results demonstrate that the precision of causality extraction can reach 81.67% and the F1 value can reach 83.2%.

A Cascading Failure Model of the Air Traffic Control Network Based on the Theory of Interdependent Networks

With the continuous development of information technology, a spontaneous interdependent network has formed within the air traffic control network. Due to the internal interdependence, any small, failed node may trigger a cascade failure of the entire system. The purpose of this study is to investigate the resilience of air traffic control networks. Based on air traffic management regulations, a new cascading failure model for air traffic control networks is proposed, which is based on the theory of interdependent networks. The model establishes a dual-layer dependency relationship between the control coordination network and the air route facility network, including control dependency and service dependency. Through experiments, targeted measures are proposed to improve the safety and reliability of air traffic control. This model introduces parameters such as control cost and node control capability, and reflects the resilience of the air traffic control network, based on the final number of failed nodes after the steady-state of the cascade failure, the network’s cascade failure rate, and the system’s load failure threshold. Simulation results show that enhancing the control capability and increasing the number of control positions can improve the control cost of the air traffic control network. The higher the control cost, the better the resilience of the air traffic control network. Improving the control capability of control nodes has a greater impact on the resilience of the air traffic control network, compared to increasing the number of control nodes. The degree attack on route nodes has a greater impact on the cascade failure of the air traffic control network, compared to random attacks and facility node degree attacks. The cascade failure model proposed in this paper provides a new method for guiding the air traffic control network to resist cascade failure attacks and enhance its resilience.

Segmentation of renal structures based on contrast computed tomography scans using a convolutional neural network

Aim. Develop a neural network to build 3D models of kidney neoplasms and adjacent structures. Materials and methods. DICOM data (Digital Imaging and Communications in Medicine standard) from 41 patients with kidney neoplasms were used. Data included all phases of contrast-enhanced multispiral computed tomography. We split the data: 32 observations for the training set and 9 – for the validation set. At the labeling stage, the arterial, venous, and excretory phases were taken, affine registration was performed to jointly match the location of the kidneys, and noise was removed using a median filter and a non-local means filter. Then the masks of arteries, veins, ureters, kidney parenchyma and kidney neoplasms were marked. The model was the SegResNet architecture. To assess the quality of segmentation, the Dice score was compared with the AHNet, DynUNet models and with three variants of the nnU-Net (lowres, fullres, cascade) model. Results. On the validation subset, the values of the Dice score of the SegResNet architecture were: 0.89 for the normal parenchyma of the kidney, 0.58 for the kidney neoplasms, 0.86 for arteries, 0.80 for veins, 0.80 for ureters. The mean values of the Dice score for SegResNet, AHNet and DynUNet were 0.79; 0.67; and 0.75, respectively. When compared with the nnU-Net model, the Dice score was greater for the kidney parenchyma in SegResNet – 0.89 compared to three model variants: lowres – 0.69, fullres – 0.70, cascade – 0.69. At the same time, for the neoplasms of the parenchyma of the kidney, the Dice score was comparable: for SegResNet – 0.58, for nnU-Net fullres – 0.59; lowres and cascade had lower Dice score of 0.37 and 0.45, respectively. Conclusion. The resulting SegResNet neural network finds vessels and parenchyma well. Kidney neoplasms are more difficult to determine, possibly due to their small size and the presence of false alarms in the network. It is planned to increase the sample size to 300 observations and use post-processing operations to improve the model.

Contagion risks and security investment in directed networks.

We develop a model for contagion risks and optimal security investment in a directed network of interconnected agents with heterogeneous degrees, loss functions, and security profiles. Our model generalizes several contagion models in the literature, particularly the independent cascade model and the linear threshold model. We state various limit theorems on the final size of infected agents in the case of random networks with given vertex degrees for finite and infinite-variance degree distributions. The results allow us to derive a resilience condition for the network in response to the infection of a large group of agents and quantify how contagion amplifies small shocks to the network. We show that when the degree distribution has infinite variance and highly correlated in- and out-degrees, even when agents have high thresholds, a sub-linear fraction of initially infected agents is enough to trigger the infection of a positive fraction of nodes. We also demonstrate how these results are sensitive to vertex and edge percolation (intervention). We then study the asymptotic Nash equilibrium and socially optimal security investment. In the asymptotic limit, agents' risk depends on all other agents' investments through an aggregate quantity that we call network vulnerability. The limit theorems enable us to capture the impact of one class of agents' decisions on the overall network vulnerability. Based on our results, the vulnerability is semi-analytic, allowing for a tractable Nash equilibrium. We provide sufficient conditions for investment in equilibrium to be monotone in network vulnerability. When investment is monotone, we demonstrate that the (asymptotic) Nash equilibrium is unique. In the specific example of two types of core-periphery agents, we illustrate the strong effect of cost heterogeneity on network vulnerability and the non-monotonous investment as a function of costs.

Sum-of-squares bounds on correlation functions in a minimal model of turbulence.

We suggest a new computer-assisted approach to the development of turbulence theory. It allows one to impose lower and upper bounds on correlation functions using sum-of-squares polynomials. We demonstrate it on the minimal cascade model of two resonantly interacting modes when one is pumped and the other dissipates. We show how to present correlation functions of interest as part of a sum-of-squares polynomial using the stationarity of the statistics. That allows us to find how the moments of the mode amplitudes depend on the degree of nonequilibrium (analog of the Reynolds number), which reveals some properties of marginal statistical distributions. By combining scaling dependence with the results of direct numerical simulations, we obtain the probability densities of both modes in a highly intermittent inverse cascade. As the Reynolds number tends to infinity, we show that the relative phase between modes tends to π/2 and -π/2 in the direct and inverse cascades, respectively, and derive bounds on the phase variance. Our approach combines computer-aided analytical proofs with a numerical algorithm applied to high-degree polynomials.

Treatment cascade for patients with multidrug- or rifampicin-resistant tuberculosis and associated factors with patient attrition in southeastern China: a retrospective cohort study

ObjectivesTo address gaps in health services for multidrug- or rifampicin-resistant tuberculosis (MDR/RR-TB), a treatment cascade model was used to evaluate patient retention and attrition at each successive step required to achieve a successful treatment outcome. MethodsFrom 2015–2018, a four-step treatment cascade model was established in patients with confirmed MDR/RR-TB in southeast China. Step 1: diagnosis of MDR/RR-TB, step 2: Initiation of treatment, step 3: still under treatment at 6 month and step 4: cure or completion of MDR/RR-TB treatment, with each successive step including a gap that shows attrition of patients between steps. The retention and attrition of each step were graphed. Multi-variate logistic regression was carried out to further identify potential factors associated with the attrition. ResultsIn the treatment cascade consisting of 1752 MDR/RR-TB patients, the overall patient attrition rate was 55.8% (978/1752), with 28.0% (491/1752), 19.9% (251/1261), and 23.4% (236/1010) of patients attrition in the first, second, and third gap. Factors associated with MDR/RR-TB patients not initiating treatment included age ≥60 years (OR:2.875), and time for diagnosis ≥30 days (OR: 2.653). Patients who were diagnosed with MDR/RR-TB through rapid molecular test (OR: 0.517) and non-migrant residents of Zhejiang Province (OR: 0.273) both exhibited a lower likelihood of attrition during the treatment initiation phase. Meanwhile, old age (OR: 2.190) and non-resident migrants to the province were factors associated with not completing ≥ 6 months of treatment. Old age (OR: 3.883), retreatment (OR: 1.440), and time to diagnosis ≥30 days (OR: 1.626) were factors contributing to poor treatment outcomes. ConclusionSeveral programmatic gaps were identified in the MDR/RR-TB treatment cascade. Future policies should provide more comprehensive support for vulnerable populations to improve the care quality at each step.

Analysis of the Short-Term Dynamics of Morphing Aircraft Caused by Shape Change Based on the Open-Loop Response and the Reachable Set Theory

This work investigates the short-term dynamics caused by shape changes of morphing aircraft. We select a symmetric variable sweep morphing aircraft as the object of study and establish a six-degree-of-freedom multi-loop cascade model, and the coupling between derivative terms is eliminated by matrix transformation. Considering that the change in aerodynamic shape significantly affects the aerodynamic forces of the aircraft in a short period of time, and the variation in mass distribution generates additional aerodynamic forces and moments, we analyze the effects of these factors on the dynamic characteristics of the aircraft based on the open-loop response starting from the steady-state flight conditions. In addition, we analyze the improvement in maneuvering performance brought by morphing as an additional control input. We apply reachable set theory to multi-loop equations of motion and use the size of the reachable set to measure the maneuverability of aircraft. The results confirm that morphing can effectively improve the maneuverability of the aircraft.

Anomalous loss behavior in a single-component Fermi gas close to a p -wave Feshbach resonance

We theoretically investigate three-body losses in a single-component Fermi gas near a $p$-wave Feshbach resonance in the interacting, non-unitary regime. We extend the cascade model introduced by Waseem \textit{et al.} [M. Waseem, J. Yoshida, T. Saito, and T. Mukaiyama, Phys. Rev. A \textbf{99}, 052704 (2019)] to describe the elastic and inelastic collision processes. We find that the loss behavior exhibits a $n^3$ and an anomalous $n^2$ density dependence for a ratio of elastic-to-inelastic collision rate larger and smaller than 1, respectively. The corresponding evolutions of the energy distribution show collisional cooling or evolution toward low-energetic non-thermalized steady states, respectively. These findings are particularly relevant for understanding atom loss and energetic evolution of ultracold gases of fermionic lithium atoms in their ground state.

Cascading failure invulnerability analysis of chemical material network considering failure propagation capability

AbstractThe material network in the chemical process has the characteristics of a complex network. Once the fault occurs, it will spread and lead to cascading failures, causing the chemical production process to not proceed normally. Previous studies of cascading failures have been limited by the size and characteristics of the network, which prevented the definition of the fault propagation capacity of the edge based on the actual situation. Studies on the effects of network topology and certain risk factors on the amount of fault propagation still need to be completed. Because of the above shortcomings, this study constructs a chemical material network cascading failure invulnerability analysis model that considers the failure propagation ability. Through the study of the chemical material network topology structure and the analysis of various flammable and explosive chemicals, the paper defines the fault propagation coefficient, which affects the load propagation quantity on the connecting edge. Then, based on the load‐capacity nonlinear cascading failure model, the chemical material network's invulnerability was analyzed by adjusting the model parameters. The effectiveness of the model is verified through case analysis. Compared to the existing model, the model in this paper effectively improves the invulnerability of the material network and can reduce the phenomenon of large‐scale failure of the material network due to the failure propagation capability is not considered.

Reciprocal relationship between interpersonal communication and depressive symptoms and the mediating role of resilience across two years: Three-wave cross-lagged panel model

BackgroundDepression among children has been a growing public health concern. It is generally recognized that individuals with depression are likely to have interpersonal malfunctioning. However, there remains a limited scientific understanding of the reciprocal relationship between interpersonal communication and depressive symptoms among rural Chinese children in a longitudinal approach. MethodsTherefore, guided by the interpersonal model of depression and the developmental cascade model, the present study conducted a cross-lagged panel analysis study to explore the bidirectional relationship between interpersonal communication and depressive symptoms across three waves among 2188 elementary school students in rural areas of one county of Gansu Province, China. We also examined the mediating effect of resilience and sex differences of the models. ResultsOur results showed that depressive symptoms negatively predicted interpersonal communication from T1 to T2 and T2 to T3. Interpersonal communication negatively predicted depressive symptoms from T1 to T2, but not T2 to T3. Furthermore, resilience showed significant partial mediating effects in the reciprocal relationship between interpersonal communication and depressive symptoms. In terms of sex differences, the significant relationship between depressive symptoms at T1 and interpersonal communication at T2 was found to be significant among male students and marginally significant among female students. The full mediating effect of resilience at T1 was found only among male students, whereas resilience at T2 functioned as a full mediator between depressive symptoms at T2 and interpersonal communication at T3 only among female students. LimitationsFirst, the present sample consisted of only third and fourth grade (i.e., in T1) students from one county in rural China. Second, the present study examined depressive symptoms instead of depression as a clinical diagnosis. Third, the third wave of the data was collected during COVID-19. The impact of the COVID-19 pandemic could unexpectedly pose on child mental health. ConclusionsThe finding underlined the importance of providing comprehensive depression prevention and intervention from fostering children's inner resilience and promoting their ability to navigate interpersonal resources.