Evolution Of Cascades Research Articles

Typhoon related cascading fault chain dynamic evolution model and risk mitigation in distribution systems

The resilience of distribution system is severely influenced by typhoon disasters and the secondary disasters such as floods and debris flows. The cascading propagation of typhoon disasters, coupled with the cascading propagation of faults in distribution systems, creates a dual-coupling dynamic that results in large-scale faults. The key to mitigating losses from typhoon-related cascading faults lies in understanding the potential paths of cascading propagation and taking corresponding measures to preemptively interrupt the chain propagation. In this paper, based on the analysis of actual typhoon related fault data of Guangzhou, China, we created the knowledge graph of the typhoon related cascading fault chains and modeled the chain formation mechanism, successfully integrating the distribution systems and the typhoon disaster propagation systems. We also achieved the dynamic evolution of typhoon related cascading fault chains by using system dynamics. In the case studies of Guangzhou, we selected three typical typhoon related fault scenarios, and then the proposed model is utilized to capture the fault cascading pathway, which can help mitigate typhoon related fault risks in distribution systems.

A Study on the Cascade Evolution Mechanism of Construction Workers’ Unsafe Behavior Risk Factors

There are numerous risk factors across various dimensions that lead to unsafe behaviors among construction workers, and the interactions between these factors are complex and intertwined. Therefore, it is crucial to comprehensively explore the mechanisms of these risk factors across all dimensions to reduce the accident rate. This paper combines cascading failure and entropy flow models to construct a cascading trigger model for identifying key nodes and paths in a risk network. First, this paper identifies the risk factors in the individual, organizational, managerial, and environmental dimensions, dividing them into deep and surface factors. Based on this, a risk network is constructed, and cascading failure is introduced to simulate the dynamic evolution of risks. Then, the entropy flow model is introduced to quantify the risk flow in risk propagation. Finally, to address the uncertainty of risk occurrence, Visual Studio Code is used for coding, and a simulation platform is built using JavaScript. After conducting simulation experiments, the results are statistically analyzed. The results show that the key nodes of deep factors are mainly concentrated in the individual dimension (herd mentality, negative emotions, physical fatigue, fluke mindset), organizational dimension (poor cohesion, poor internal communication), and managerial dimension (abusive leadership style and insufficient/low-quality safety education and training); the surface factors are mainly the poor safety climate in the organizational dimension. The findings provide theoretical support for reducing the accident rate caused by unsafe worker behaviors, aiming to reduce accident risk losses by cutting off risk propagation paths.

Exploring Dynamic Capability Drivers of Green Innovation at Different Digital Transformation Stages: Evidence from Listed Companies in China

Digital transformation has emerged as a pivotal catalyst for corporate green innovation, specifically in the context of the green development of the manufacturing industry. Nevertheless, it is evident that there are significant disparities in the various stages of corporate digital transformation. Furthermore, the precise dynamic capabilities required to propel corporate green innovation at distinct stages of this transformation, along with their underlying influencing mechanisms, remain ambiguous. Drawing on dynamic capabilities, this paper delves into the inherent mechanism of corporate green innovation based on the data of listed companies in the Chinese manufacturing industry. The study reaches the following conclusions: (1) The driving process of digital transformation in green innovation exhibits distinct stage characteristics. Digital transformation significantly enhances the quantity of green innovation in the steering period and has a significant impact on both the quantity and quality of green innovation in the shaping and upgrading periods. Moreover, the shaping period has a stronger impact on quantity, while the upgrading period has a stronger impact on quality. (2) There is an evident evolution and upgrading of dynamic capabilities as digital transformation progresses. Among these capabilities, adaptive capability plays a mediating role in the steering period, and innovative capability plays a mediating role in the upgrading period. (3) Top management teams’ environmental attention plays a positive moderating role in digital-transformation-driven green innovation by strengthening absorptive and innovative capabilities. This study reveals the cascading evolution of dynamic capabilities in the digital transformation stage, explores the synergistic effect of top management teams’ environmental attention and specific dynamic capabilities, and provides management strategies for the “quantitative growth and qualitative enhancement” of corporate green innovation.

A modified two temperature molecular dynamics (2T-MD) model for cascades

Two-Temperature molecular dynamics (2T-MD) is a common approach for describing how electrons contribute to the evolution of a damage cascade by addressing their role in the redistribution of energy in the system. However, inaccuracies in 2T-MD’s treatment of the high-energy particles have limited its utilisation. Here, we propose a reformulation of the traditional 2T-MD scheme to overcome this limitation by addressing the spurious double-interaction of high-energy atoms with electrons. We conduct a series of radiation damage cascades for 30, 50, and 100 keV primary knock-on atoms in increasingly large cubic W cells. In the simulations, we employ our modified 2T-MD scheme along with other treatments of electron–phonon coupling to explore their impact on the cascade evolution and the number of remnant defects. The results suggest that with the proposed modification, 2T-MD simulations account for the temperature time evolution during the ballistic phase and remove arbitrary choices, thus providing a better description of the underlying physics of the damage process.

Radiation-induced defects in the InGaN/GaN superlattice structure

With the molecular dynamics method, this paper investigates radiation-induced defects in the In0.16Ga0.84N/GaN superlattice structure (SLS) and the In0.04Ga0.96N/GaN SLS. In the temporal evolution of cascades, most of vacancies recombine with interstitials. The Monte Carlo simulations about the proportions of PKAs induced by 3 MeV protons were also considered in this work for calculating the weighted averages of surviving defects. For the In0.16Ga0.84N/GaN SLS irradiated by protons, around 82.6 percent of surviving vacancies are Ga vacancies while around 88.9 percent of surviving interstitials are Ga interstitials. For the In0.04Ga0.96N/GaN SLS irradiated by protons, around 87.3 percent of surviving vacancies are Ga vacancies while around 88.6 percent of surviving interstitials are Ga interstitials. N vacancies, N interstitials, and In vacancies also exist in irradiated InGaN/GaN SLS. Details about different types of defects are presented in this paper, which helps explain the microscopic mechanism of irradiated InGaN/GaN SLS. Since different types of defects have different influences on electronic and optical properties, simulations about the proportions of various defects in irradiated InGaN/GaN SLS help experimentalists find the effective factors of radiation-related changes in electronic and optical properties.

Socio-ecological risk analysis framework coupled with ecosystem services

The strong coupling between society and ecosystem makes socio-ecological risks become the main object of risk management. As the link between ecological and social processes, ecosystem services (ESs) are the core variable in deconstructing the social-ecological risks and the crucial point in resolving the risks. We explored the concept and the internal formation mechanisms of socio-ecological risk combining ESs, and further put the cascade logic and evolution process of "real risk-risk perception-risk behavior". Based on driver-pressure-state-impact-response framework (DPSIR), we proposed a framework for analyzing socio-ecological risk, and expanded the content and methodology system of research and management practices related to socio-ecological risks. We proposed that socio-ecological risk research coupled with ESs should focus on: 1) exploring the transmission mechanism between ecosystem processes, ecosystem services, and human well-being; 2) exploring the response mechanism of social subject behavior and its impacts on ecosystem services and human well-being; 3) construction of a multi-scale assessment model for social ecological risks coupled with ESs. The socio-ecological risk analysis framework for coupled ecosystem services was based on the mutual feedback between human and nature to explore the logic of risk formation, evolution, and governance, which could provide ideas for clarifying the deep meaning of ecological problems and selecting pathways to resolve socio-ecological risks.

Development of a multi-layer network model for characterizing energy cascade behavior on turbulent mixing

Eddies of various sizes are visible to the naked eye in turbulent flow. Each eddy scale corresponds to a fraction of the total energy released by the turbulence cascade. Understanding the dynamic mechanism of the energy cascade is crucial to the study of turbulent mixing. In this paper, an energy cascade multi-layer network (ECMN) based on the complex network algorithm is proposed to investigate the spatio-temporal evolution of the energy cascade, covering both the inertial and dispersive ranges. The dynamic process of energy cascade is transformed into a topological structure based on the node definition and edge determination. The topological structure allows for the exploration of eddies interaction and chaotic energy transfer across scales. The model results show the intermittent and non-uniform nature of the energy cascade. Meanwhile, the scale gap found in the model verifies the fractal property of the energy evolution. We also found that scales of the generated eddies in energy cascade process are stochastic, and a synchronous energy cascade pattern is demonstrated according to the constructed framework. Furthermore, it provides a topological way to evaluate the contribution of large and small scale eddies. In addition, a network structure coefficient κ is proposed to evaluate the energy transfer strength. It agrees very well with the fluctuation of dissipation rates. All of this shows that the network model can effectively reveal the inhomogeneous properties of the energy cascade and quantify the turbulent mixing intensity based on the intermittent scale interaction. This also provides new insights into the study of fractal scales of nonlinear complex systems and the bridging of chaotic dynamics with topological frameworks.

Grain boundary effects on defect production and damage cascade evolution in SiC/PyC interface: A molecular dynamics study

In this study, molecular dynamics simulations were employed to investigate the effect of symmetrical tilt grain boundaries (STGBs) on the cascade collision evolution at the SiC/PyC interface. We observed that the tilt angle size of grain boundary (GB) spatial structures significantly influences both the type and number of defects caused by primary knock-on atom (PKA) collisions at the interface, altering the cascade damage morphology. Under the PKA range from 1.5[Formula: see text]keV to 15[Formula: see text]keV at 1000[Formula: see text]K, the interplay between GB and interface damage throughout various cascade collision stages impacts defect generation and PKA efficiency. Integrating the analyses of displacement cascade morphology, threshold displacement energy (TDE), and Frenkel pairs (FPs) evolution, it is evident that GBs introduced into the SiC/PyC interface with single crystals exhibit reduced defect absorption efficiency. This implies the existence of competing mechanisms of GB damage and interfacial damage. Notably, the GB plane near the interface exhibits enhanced irradiation resistance and atomic arrangement stability compared to areas without GB. Overall, our results offer crucial insights into the irradiation resistance mechanics of ceramic composite interfaces, laying the groundwork for future studies.

The Spatiotemporal Evolution Mechanism of Urban Rail Transit Fault Propagation in Networked Operation Modes

The cascading propagation and evolution of metro operation failures can significantly impact the safety of metro operation. To overcome this challenge, this study pre-processes a massive amount of metro operation log data through noise reduction. Moreover, a professional terminology dictionary is constructed along with a custom stop-word dictionary to segment the preprocessed data. Subsequently, the AFP-tree algorithm is employed to mine the segmented log data and identify key hazards. A weighted urban rail transit network is established, considering the effective path time cost, and the shortest travel OD path. To simulate the dynamic evolution of the failure chain propagation, a model based on disaster propagation theory is constructed. Taking the Shanghai Metro line as a case, multiple simulation scenarios are established with 25 key hazards as triggering points, and the number of cascade failure stations affected under different scenarios is outputted. The results indicate that the fault stations caused by the large passenger flow are the largest. Meanwhile, the number of stations affected by the door clamp is the smallest. The scale of fault stations reaches a maximum value in 16–20 min. Through case analysis, a positive correlation is found when the self-recovery factor is between 14 and 18, and the number of fault stations shows a significant increasing trend. The research results can provide decision-making support and theoretical guidance for rail transit operation safety management enterprises.

Self-similar evolution of wave turbulence in Gross-Pitaevskii system.

We study the universal nonstationary evolution of wave turbulence (WT) in Bose-Einstein condensates (BECs). Their temporal evolution can exhibit different kinds of self-similar behavior corresponding to a large-time asymptotic of the system or to a finite-time blowup. We identify self-similar regimes in BECs by numerically simulating the forced and unforced Gross-Pitaevskii equation(GPE) and the associated wave kinetic equation(WKE) for the direct and inverse cascades, respectively. In both the GPE and the WKE simulations for the direct cascade, we observe the first-kind self-similarity that is fully determined by energy conservation. For the inverse cascade evolution, we verify the existence of a self-similar evolution of the second kind describing self-accelerating dynamics of the spectrum leading to blowup at the zero mode (condensate) at a finite time. We believe that the universal self-similar spectra found in the present paper are as important and relevant for understanding the BEC turbulence in past and future experiments as the commonly studied stationary Kolmogorov-Zakharov (KZ) spectra.

Molecular dynamics simulation of primary irradiation damage in Ti-6Al-4V alloys

Displacement cascade behaviors of Ti-6Al-4V alloys are investigated using molecular dynamics (MD) simulation. The embedded atom method (EAM) potential including Ti, Al and V elements is modified by adding Ziegler-Biersack-Littmark (ZBL) potential to describe the short-range interaction among different atoms. The time evolution of displacement cascades at the atomic scale is quantitatively evaluated with the energy of primary knock-on atom (PKA) ranging from 0.5 keV to 15 keV, and that for pure Ti is also computed as a comparison. The effects of temperature and incident direction of PKA are studied in detail. The results show that the temperature reduces the number of surviving Frenkel pairs (FPs), and the incident direction of PKA shows little correlation with them. Furthermore, the increasing temperature promotes the point defects to form clusters but reduces the number of defects due to the accelerated recombination of vacancies and interstitial atoms at relatively high temperature. The cluster fractions of interstitials and vacancies both increase with the PKA energy, whereas the increase of interstitial cluster is slightly larger due to their higher mobility. Compared to pure Ti, the presence of Al and V is beneficial to the formation of interstitial clusters and indirectly hinders the production of vacancy clusters.

Electronic effects on radiation damage in α-iron: A molecular dynamics study

Iron (Fe)-based alloys, which have been widely used as structural materials in nuclear reactors, can significantly change their microstructure properties and macroscopic properties under high flux neutron irradiation during operation, thus, the problems associated with the safe operation of nuclear reactors have been put forward naturally. In this work, a molecular dynamics simulation approach combined with electronic effects is developed for investigating the primary radiation damage process in α-Fe. Specifically, the influence of electronic effects on the collision cascade in Fe is systematically evaluated based on two commonly used interatomic potentials for Fe. The simulation results reveal that both electronic stopping (ES) and electron–phonon coupling (EPC) can contribute to the decrease of the number of defects in the thermal spike phase. The application of ES reduces the number of residual defects after the cascade evolution, whereas EPC has a reverse effect. The introduction of electronic effects promotes the formation of the dispersive subcascade: ES significantly changes the geometry of the damaged region in the thermal spike phase, whereas EPC mainly reduces the extent of the damaged region. Furthermore, the incorporation of electronic effects effectively mitigates discrepancies in simulation outcomes when using different interatomic potentials.

Bicyclo[3.2.0]carbocyclic Molecules and Redox Biotransformations: The Evolution of Closed-Loop Artificial Linear Biocatalytic Cascades and Related Redox-Neutral Systems.

The role of cofactor recycling in determining the efficiency of artificial biocatalytic cascades has become paramount in recent years. Closed-loop cofactor recycling, which initially emerged in the 1990s, has made a valuable contribution to the development of this aspect of biotechnology. However, the evolution of redox-neutral closed-loop cofactor recycling has a longer history that has been integrally linked to the enzymology of oxy-functionalised bicyclo[3.2.0]carbocyclic molecule metabolism throughout. This review traces that relevant history from the mid-1960s to current times.

Experimental Study on the Influence of Incoming Flow on Wind Turbine Power and Wake Based on Wavelet Analysis

Taking a wind farm in the Qinghai–Tibet Plateau as the experimental site, the ZephiR Dual Mode (ZDM) LiDAR and ground-based laser LiDAR were used to scan the incoming flow and wake of the wind turbine separately. Based on wavelet analysis, the experimental study was conducted on the influence of different incoming wind speeds on the power and wake of the wind turbine. It is found that the incoming wind speeds have a great influence on the wind turbine power, and the fluctuation frequency of the wind speed is obviously higher than that of the power, that is, the scale effects of turbulence are magnified. The rotation of the wind wheel can accelerate the collapse of the large-scale turbulent structures of the incoming flow, and large-scale vortices continue to collapse into small-scale vortices, that is, the energy cascade evolution occurs. And in the wake diffusion process, the dissipation degree of the upper blade tip vortex is greater than that of the lower blade tip vortex caused by the rotation of the wind turbine. Under the same incoming flow conditions, due to the influence of tower and ground turbulence structure, the energy level connection phenomenon of the measuring points below the hub height is stronger than that above the hub height, and it weakens with the increase of the measuring distance. That is, the energy cascade of the measuring points below the hub height at 1.5 D (D is the diameter of the wind wheel) of the wake is weaker than that at 1 D of the wake. With the increase of the measuring distance of the wake, the influx of the external flow field further aggravates the momentum exchange and energy transport between the vortex clusters, that is, the influence of the external flow field gradually increases in the wake vortex pulsation.

The Impact of Therapeutic Plasma Exchange on Inflammatory Markers and Acute Phase Reactants in Patients with Severe SARS-CoV-2 Infection.

Background and Objectives: Due to the poor prognosis and the very high mortality rate associated with severe SARS-CoV-2 infections, various regimens have been tried to stop the evolution of the inflammatory cascade, such as immunomodulatory therapy and plasma clearance of the acute phase reactants involved. Therefore, the objective of this review was to analyze the effects of using therapeutic plasma exchange (TPE), also known as plasmapheresis, on the inflammatory markers of critically ill COVID-19 patients admitted to the intensive care unit (ICU). Materials and Methods: A thorough scientific database search was performed, and it included a review of articles published on PubMed, Cochrane Database, Scopus, and Web of Science from the beginning of the COVID-19 pandemic in March 2020 until September 2022 that focused on the treatment of SARS-CoV-2 infections using plasma exchange for patients admitted to the ICU. The current study included original articles, reviews, editorials, and short or special communications regarding the topic of interest. Results: A total of 13 articles were selected after satisfying the inclusion criterion of three or more patients enrolled with clinically severe COVID-19 that were eligible for TPE. From the included articles, it was observed that TPE was used as a last-resort salvage therapy that can be regarded as an alternative treatment method when the standard management for these patients fails. TPE significantly decreased the inflammatory status as measured by Interleukin-6 (IL-6), C-reactive protein (CRP), lymphocyte count, and D-dimers, as well as improving the clinical status measured with PaO2/FiO2 and duration of hospitalization. The pooled mortality risk reduction after TPE was 20%. Conclusions: There are sufficient studies and evidence to show that TPE reduces inflammatory mediators and improves coagulation function and the clinical/paraclinical status. Nevertheless, although it was shown that TPE decreases the severe inflammatory status without significant complications, the improvement of survival rate remains unclear.

Quickest Inference of Network Cascades With Noisy Information

We study the problem of estimating the source of a network cascade given a time series of noisy information about the spread. Initially, there is a single vertex affected by the cascade (the source) and the cascade spreads in discrete time steps across the network. The cascade evolution is hidden, but one can observe a time series of noisy signals from each vertex. The time series of a vertex is assumed to be a sequence of i.i.d. samples from a pre-change distribution $Q_0$ before the cascade affects the vertex, and the time series is a sequence of i.i.d. samples from a post-change distribution $Q_1$ once the cascade has affected the vertex. Given the time series of noisy signals, which can be viewed as a noisy measurement of the cascade evolution, we aim to devise a procedure to reliably estimate the cascade source as fast as possible. We investigate Bayesian and minimax formulations of the source estimation problem, and derive near-optimal estimators for simple cascade dynamics and network topologies. In the Bayesian setting, an estimator which observes samples until the error of the Bayes-optimal estimator falls below a threshold achieves optimal performance. In the minimax setting, optimal performance is achieved by designing a novel multi-hypothesis sequential probability ratio test (MSPRT). We find that these optimal estimators require $\log \log n / \log (k - 1)$ observations of the noisy time series when the network topology is a $k$-regular tree, and $(\log n)^{\frac{1}{\ell + 1}}$ observations are required for $\ell$-dimensional lattices. Finally, we discuss how our methods may be extended to cascades on arbitrary graphs.

Molecular dynamics simulation of effects of Al on the evolution of displacement cascades in AlxCoCrFeNi high entropy alloys

This work studied the evolution of displacement cascades in AlxCoCrFeNi high entropy alloys (HEAs) with different Al contents by molecular dynamics (MD) simulation to reveal the effects of Al and its-induced microstructural variations on the primary structure damage of HEAs exposed to energetic particle irradiations. Both the amount of peak Frenkel pairs and recombination efficiency of Frenkel pairs in the displacement cascades are increased with Al content, which are induced by the decrease of atomic displacement energies and prolonged thermal spike, respectively. The amount of surviving defects depends on the competition between the two effects. When the PKA energies are 5 and 10 keV, both effects are comparable, resulting in that the amount of surviving defects first decreases and then increases with Al content. The minimum amount of surviving defects is present for Al content of 5 at. %. When the PKA energy is further increased to 20 keV, the increase of peak Frenkel pairs becomes dominant, resulting in the amount of surviving defects increases monotonically with Al content. Moreover, the vacancy clustering is restrained but the interstitial clustering is promoted with increasing Al content. The current results indicate that the addition of oversized alloying elements could introduce two opposite effects in the primary damage stage of HEAs, which should be considered in the design of novel HEAs with higher irradiation resistances.

Robustness of networks with dependency groups considering fluctuating loads and recovery behaviors

Dependency groups describe different characteristics of interactions among nodes, which provide an emerging way to explore the dynamical behaviors of complex networks. To study the effects of dependency groups on the robustness of flow networks, this paper proposes a cascading failure model of networks which combines fluctuating loads and dependency groups. Different from the previous hypothesis that the dependency group is completely invalid once one node in the same dependency group fails, in this paper, the failure and recovery mechanism of dependency groups under certain rules to prevent catastrophic collapses of networks is proposed. To describe the resistance of network to damage caused by cascading failures, we introduce the overload coefficient to characterize the overload state when the node handles excess loads. Considering the network cost should be controlled within a reasonable range while improving network robustness, the cost index based on the relationship between the load and capacity of the node is established. By theoretically analyzing the network cost, the relationship between the network robustness and network cost is discussed when the network cascading process happens. The proposed model is employed to study the dynamics of cascading failures evolution in BA network, ER network and two actual networks. Simulation results reveal the effects of traffic flows and dependency groups on the dynamic loads propagation of cascading failures.

Scenario-Driven Methodology for Cascading Disasters Risk Assessment of Earthquake on Chemical Industrial Park

With the increase in industrial accidents induced by natural disasters, the study of earthquake risk assessment has been widely considered by scholars. However, the cascade evolution of Natech (natural–technological) disasters has not been thoroughly studied, especially in chemical parks with complex technological processes. From the perspective of scenario deduction, combined with cross-impact analysis and a damping interpretation structural model, this paper analyzes the evolution process of cascade disaster in a chemical industrial park after the Wenchuan earthquake. At the same time, a visual network risk assessment model is constructed to identify the impact of earthquake cascade disasters on the park. The simulation results show that the scenario-driven risk assessment method proposed in this paper can directly reflect the coupling relationship and propagation path among the derived events and realize dynamic, intuitive and structured disaster expression to deal with the earthquake Natech (natural–technological) disaster scenario effectively and quickly.

Hierarchical attention neural network for information cascade prediction

Online social networking platforms have drastically facilitated the phenomenon of information cascades, making cascade prediction an important task for both researchers and practitioners. In cascade propagation paths, influential users can often attract more attention. Moreover, the community structure formed by users with similar interests creates information redundancy, thereby restricting the propagation process. These two features greatly affect the growth of the cascades. This paper investigates the incremental prediction of cascades during a future time period based on the evolution of cascades in early stages and proposes a neural network framework with hierarchical attention mechanisms, named Hierarchical Attention Cascade Neural Network (CasHAN). This network has a node-level attention mechanism based on user influence and a sequence-level attention mechanism based on community redundancy. User influence considers the user’s own attributes and the influence feedback provided by neighbors, while community redundancy measures information redundancy characteristics that limit cascade propagation. Through hierarchical attention mechanisms, the effective combination of these two features improves the accuracy of cascade increment prediction. Extensive experiments on real-world datasets demonstrate that our approach outperforms other state-of-the-art prediction methods.