Cascade Model Research Articles

Inviscid evolution of a uniform vortex dipole in a strain field

Competing models employing anti-parallel vortex collision in search of a finite-time singularity of Euler's equation have arisen recently. Both the vortex sheet model proposed by Brenner et al. (Phys. Rev. Fluids, vol. 1, 2016, 084503) and the ‘tent’ model proposed by Moffatt & Kimura (J. Fluid Mech., vol. 861, 2019, pp. 930–967) consider a vortex monopole exposed to a strain flow to model the evolution of interacting anti-parallel vortices, a fundamental element in the turbulent cascade. Herein we employ contour dynamics to explore the inviscid evolution of a vortex dipole subjected to an external strain flow with and without axial stretching. We find that for any strain-to-vorticity ratio $\mathcal {E}$ , the constituent vortices compress indefinitely, with weaker strain flows causing flattening to occur more slowly. At low $\mathcal {E}$ , the vortex dipole forms the well-documented head–tail structure, whereas increasing $\mathcal {E}$ results in the dipole compressing into a pair of vortex sheets with no appreciable head structure. Axial stretching effectively lowers $\mathcal {E}$ dynamically throughout the evolution, thus delaying the transition from the head–tail regime to the vortex sheet regime to higher strain-to-vorticity ratios. Findings from this study offer a bridge between the two cascade models, with the particular mechanism arising depending on $\mathcal {E}$ . It also suggests limits for the ‘tent’ model for a finite-time singularity, wherein the curvature-induced strain flow must be very weak in comparison with the vorticity density-driven mutual attraction such that the convective time scale of the evolution exceeds the core flattening time scale.

Geometric deep learning for online prediction of cascading failures in power grids

Past events have revealed that widespread blackouts are mostly a result of cascading failures in the power grid. Understanding the underlining mechanisms of cascading failures can help in developing strategies to minimize the risk of such events. Moreover, a real-time detection of precursors to cascading failures will help operators take measures to prevent their propagation. Currently, the well-established probabilistic and physics-based models of cascading failures offer low computational efficiency, hindering them to be used only as offline tools. In this work, we develop a data-driven methodology for online estimation of the risk of cascading failures. We utilize a physics-based cascading failure model to generate a cascading failure dataset considering different operating conditions and failure scenarios, thus obtaining a sample space covering a large set of power grid states that are labeled as safe or unsafe. We use the synthetic data to train deep learning architectures, namely Feed-forward Neural Networks (FNN) and Graph Neural Networks (GNN). With the development of GNNs, improved performance is achieved with graph-structured data, and GNNs can generalize to graphs of diverse sizes. A comparison between FNN and GNN is made and the GNNs inductive capability is demonstrated via test grids. Furthermore, we apply transfer learning to improve the performance of a pre-trained GNN model on power grids not seen in the training process. The GNN model shows accuracy and balanced accuracy above 96% on selected test datasets not used in the training. Conversely, the FNN shows accuracy above 85% and balanced accuracy above 81% on test datasets unseen during training. Overall, the GNN model is successful in determining, if one or several simultaneous outages result in a critical grid state, under specific grid operating conditions.

Cascading failure model and resilience enhancement scheme of space information networks

Space information networks provide global users with high-speed Internet services. The failure of a small number of satellites can trigger cascading failures that can bring down the network. Therefore, enhancing the network’s resilience under cascading failures is crucial. In this paper, we establish a cascading failure model for space information networks for the first time. The gravity model-based load calculation method, entity failure mechanisms, and new survivability measures are presented. The load unbalances of satellites are the leading cause of cascading failures, and the layout of the gateways determines the final load distribution. Therefore, a load-oriented gateway layout optimization model is developed. In order to obtain better optimization performance, a gateway layout solution algorithm based on the cuckoo search is given. Exhaustive simulations are carried out in a case satellite constellation, and the results show that the proposed approach is reasonable and able to enhance the resilience of the network under cascading failures.

Influence Maximization in Multiagent Systems by a Graph Embedding Method: Dealing With Probabilistically Unstable Links.

This article is concerned with the influence maximization (IM) problem under a network with probabilistically unstable links (PULs) via graph embedding for multiagent systems (MASs). First, two diffusion models, the unstable-link independent cascade (UIC) model and the unstable-link linear threshold (ULT) model, are designed for the IM problem under the network with PULs. Second, the MAS model for the IM problem with PULs is established and a series of interaction rules among agents are built for the MAS model. Third, the similarity of the unstable structure of the nodes is defined and a novel graph embedding method, termed the unstable-similarity2vec (US2vec) approach, is proposed to tackle the IM problem under the network with PULs. According to the embedding results of the US2vec approach, the seed set is figured out by the developed algorithm. Finally, extensive experiments are conducted to: 1) verify the validity of the proposed model and the developed algorithms and 2) illustrate the optimal solution for IM under different scenarios with PULs.

Improving resilience of high-speed train by optimizing repair strategies

High-speed trains (HSTs) are used to transport passengers to destinations efficiently and safely. However, when HST units fail, strategies for repairing them are not available. Hence, to optimize repair strategies, an interdependent network is used to model an HST, and a metric of resilience based on network theory is introduced. Based on practical factors, an interdependent machine–electricity–communication network (IMECN) and related cascading failure models are developed. Comprehensive robustness metrics for the IMECN and nodes are proposed considering the topology of networks and functional features of nodes. Accordingly, a resilience optimization model of the IMECN subject to disturbances is formulated and solved using a tabu search (TS) algorithm. Finally, a real-world HST is considered to analyze the optimal repair strategy for different numbers of failed nodes. Analysis results show that the recovery process under the optimal repair strategy has at most three stages. When a few nodes and less than half of the nodes fail, the highest and lowest levels of the IMECN are achieved, respectively. Moreover, the characteristics of the preferentially repaired node in terms of importance, vulnerability, network type, and interdependency are analyzed. Results indicate that the optimal repair strategy is not necessarily determined by topological metrics.

Influence Minimization With Conformity-Aware Competitions

Negative information (such as rumors that prevail) may produce tremendous damage in society incredibly quickly. One practical way to control negative information is to add positive information into the spread mechanism, which enjoys both effectiveness and cost-economic counterbalance. This article presents a novel conformity-aware competitive independent cascade model to simulate the spread process of two items (e.g., truth and rumor). Two advanced algorithms named the rumor-aware betweenness first (RBF) and conformity-aware demand degree (CDD) are proposed to solve the corresponding influence minimization problem. Extensive experiments conducted on seven synthetic/real-world networks have demonstrated that first, the proposed CDD or RBF algorithm outperforms other state-of-the-art methods in terms of rumor containment and efficiency in running speed; second, the time delay of rumor detection is entitled to be concerned about and costly measures are necessary if rumors have diffused chronically; and third, authoritative truth information plays a critical role in competing with rumors, relying on truth seeds will generate negative feedback when their credibility ratio is less than a specific threshold.

On the reduction of the noise in a low-pressure turbine cascade associated with the wavy leading edge

The low-pressure turbine (LPT) has become a potential noise source for future ultra-high by-pass ratio engines. In this paper, the feasibility and mechanism of wavy leading edge (WLE) noise control in the LPT cascade model are analyzed. The flow field and acoustic data are obtained with the large eddy simulation and Ffowcs Williams–Hawkings methods, which are validated using experimental data. The acoustic results are compared for different models; the maximum noise reduction can achieve 8.6 and 3.7 dBA in the frequency bands of FR#2 (315–4000 Hz) and FR#4 (6300–16 000 Hz), respectively; the noise reduction does not vary proportionally to the WLE parameter. The noise source is identified in the baseline model, and then the effect of WLE amplitude and wavelength on the noise source and its control on pressure fluctuations are evaluated. The pressure statistics demonstrate that WLE with a smaller wavelength and a larger amplitude can reduce the impingement of stator wakes on the leading edge of the rotor and stabilize the pressure fluctuation. To analyze the mechanism of WLEs on noise control, the pressure spectrum in terms of amplitude and coherence coefficient is utilized to explain the excellent noise performance of the WLE model in FR#2. The proposed similarity coefficient of coherence can quantify the destructive interference level and thus the coherence characteristics of the sound source. Generally, the noise reduction level can be predicted by the combination of the similarity coefficient and the amplitude spectrum of the pressure fluctuations for the WLE models.

Towards A Behavioural Screening Tool for Primary Psychological Triage

Rationale: Following a mass casualty event, the primary psychological triage of survivors based on clinical symptomatology is difficult for volunteer rescuers who lack the necessary knowledge of mental health issues and are under significant time pressure. To address this concern, descriptive screening tools are needed. In this context, propositions for a triage tool have taken the form of means to describe traumatic exposure or the survivors’ comorbidity factors. Objective: Alternatively, we propose a behaviour-based screening tool built on the Defence Cascade Model. This model assumes that survivors’ behaviours involve a range of motor and physiological defensive reactions. After developing this new kind of screening tool, we tested its reliability and validity. Method: Forty professionals who usually intervene in early psychological intervention units randomly rated a series of survivors’ profiles with either the behavioural tool or a tool based on classical categories of symptoms used as a control condition. The two screening tools were developed by a group of nine experts. Inter-rater reliability (unweighted kappa and Fleiss’ Kappa indexes), criterion validity (Spearman’s Rho and Kendall’s Tau indexes), face validity (subjective evaluation) and construct validity (modal classes of items) were calculated and compared between the two tools. Results: The results show that behavioural screening is significantly more reliable, while showing a significant correlation with symptom-based screening in terms of prioritisation. It also appears to be simpler than symptom screening, with most references to similar clinical concepts.

Multiparameter Estimation-Based Sensorless Adaptive Direct Voltage MTPA Control for IPMSM Using Fuzzy Logic MRAS

This paper introduces a parameter-estimation-based sensorless adaptive direct voltage maximum torque per ampere (MTPA) control strategy for interior permanent magnet synchronous machines (IPMSMs). In direct voltage control, the motor’s electrical parameters, speed, and rotor position are of great significance. Thus, any mismatch in these parameters or failure to acquire accurate speed or position information leads to a significant deviation in the MTPA trajectory, causing high current consumption and hence affecting the performance of the entire control system. In view of this problem, a fuzzy logic control-based cascaded model reference adaptive system (FLC-MRAS) is introduced to mitigate the effect of parameter variation on the tracking of the MTPA trajectory and to provide precise information about the rotor speed and position. The cascaded scheme consists of two parallel FLC-MRAS for speed and multiparameter estimation. The first MRAS is utilized to estimate motor speed and rotor position to achieve robust sensorless control. However, the speed estimator is highly dependent on time-varying motor parameters. Therefore, the second MRAS is designed to identify the quadratic inductance and permanent magnet flux and continuously update both the speed estimator and control scheme with the identified values to ensure accurate speed estimation and real-time MTPA trajectory tracking. Unlike conventional MRAS, which uses linear proportional-integral controllers (PI-MRAS), an FLC is adopted to replace the PI controllers, ensuring high estimation accuracy and enhancing the robustness of the control system against sudden changes in working conditions. The effectiveness of the proposed scheme is evaluated under different speed and torque conditions. Furthermore, a comparison against the conventional PI-MRAS is extensively investigated to highlight the superiority of the proposed scheme. The evaluation results and our quantitative assessment show the ability of the designed strategy to achieve high estimation accuracy, less oscillation, and a faster convergence rate under different working conditions. The quantitative assessment reveals that the FLC-MRAS can improve the estimation accuracy of speed, permanent magnet flux, and quadratic inductance by 19%, 55.8% and 44.55%, respectively.

Developmental Trajectories of Internalizing and Externalizing Symptoms in Youth and Associated Gender Differences: A Directed Network Perspective.

Psychopathology in youth is highly prevalent and associated with psychopathology in adulthood. However, the developmental trajectories of psychopathology symptoms, including potential gender differences, are markedly underspecified. The present study employed a directed network approach to investigate longitudinal relationships and gender differences among eight transdiagnostic symptom domains across three years, in a homogenous age sample of youth participants (n = 6,414; mean baseline age = 10.0years; 78.6% White; Adolescent Brain Cognitive Development study). Anxious/depressed problems and aggressive behaviors were central symptoms and most predictive of increases in other symptom clusters at later timepoints. Rule-breaking behaviors, aggressive behaviors, and withdrawn/depressed problems emerged as bridge symptoms between externalizing and internalizing problems. Results supported cascade models in which externalizing problems predicted future internalizing problems, but internalizing problems also significantly predicted future externalizing problems, which is contrary to cascade models. Network structure, symptom centrality, and patterns of bridge symptoms differed between female and male participants, suggesting gender differences in the developmental trajectories of youth psychopathology. Results provide new insights into symptom trajectories and associated gender differences that may provide promising pathways for understanding disorder (dis)continuity and co-occurrence. The central and bridge symptoms identified here may have important implications for screening and early intervention for youth psychopathology.

Data-driven approach to spatiotemporal dynamic risk assessment of urban flooding based on shared socio-economic pathways

Driven by the change in intense land use and land cover (LULC) due to fast urbanization, urban flooding events have become the most frequent and influential hazards over the last few decades. Accurately predicting possible flood-prone locations under the dynamic fluctuations of LULC is crucial for sustainable urban development. However, there has been sparse studies on systematic integration of LULC changes into anticipate urban development scenarios coupled with flooding vulnerability assessment. Therefore, this study proposed a robust and powerful cascade modeling chain consisting of Maximum Entropy, System Dynamics and Patch-generating Land Use Simulation in combination with shared socio-economicpathwaysfor projecting temporal and spatial dynamic changes associated with urban flooding vulnerability. Taking Guangdong Hong Kong Macao Greater Bay Area (GBA) as case study, the results showed that the increase in urban flooding was largely caused by the expansion of construction land. Overall, a substantial distinction within the scenarioswas observed and the flooding vulnerability was ranked in the order of SSP126 < SSP245 < SSP585. Under SSP585, the areas with high flooding risk will be expected to increase significantly,accounting for26% of the total areas,and nearly half of the built-upareas are exposed to flooding risk by 2050.Under SSP245, the built-up areas exposed tomedium and high flooding riskwere anticipated to cover nearly a fifth of the total areas.Under SSP126 scenario, nearly no change was predicted in the area with high flooding risk, with only increase of 1%,future urbanization hotspots associated with serious flooding risks will likely to be found on thefringeof the GBA’s construction areas,in line with the extent of construction land expansion.The finding of this study will shed a comprehensive insight into the identification of spatial and temporal distribution of urban flooding vulnerability to facilitate the exploration of flood mitigation measures associated with dynamic changes in urban land use.

An Independent Cascade Model of Graph Burning

Graph burning was introduced to simulate the spreading of news/information/rumors in social networks. The symmetric undirected graph is considered here. That is, vertex u can spread the information to vertex v, and symmetrically vertex v can also spread information to vertex u. When it is modeled as a graph burning process, a vertex can be set on fire directly or burned by its neighbor. Thus, the task is to find the minimum sequence of vertices chosen as sources of fire to burn the entire graph. This problem has been proved to be NP-hard. In this paper, from a new perspective, we introduce a generalized model called the Independent Cascade Graph Burning model, where a vertex v can be burned by one of its burning neighbors u only if the influence that u gives to v is larger than a given threshold β≥0. We determine the graph burning number with this new Independent Cascade Graph Burning model for several graphs and operation graphs and also discuss its upper and lower bounds.

Quasi-three-dimensional loss prediction model of subsonic compressor cascade based on bidirectional long short-term memory networks and multi-head self-attention

The prediction of compressor cascade loss is a crucial aspect of compressor design. Flow separation is an important flow structure and the main source of loss in subsonic cascades. In order to capture the flow separation and accurately evaluate flow loss, a data-driven quasi-three-dimensional (quasi-3D) subsonic compressor cascade loss prediction model based on bidirectional long short-term memory (BiLSTM) and multi-head self-attention is proposed. The model contains four sub-models to predict the pressure, temperature, axial velocity, and total pressure loss coefficient in two-dimensional slices along the axial direction, using Mach number, curved blade angle, solidity, camber angle, and incidence as inputs, respectively. For the purpose of adapting to cascade geometrical change, geometric reformulation is adopted before the model training. The model is trained and tested by validated computational fluid dynamics results, which contain symmetric separation and asymmetric separation samples. It is proved that the model is able to accurately predict flow parameters value in each slice. Then, four typical cases are mainly discussed, which shows that the model can effectively capture the characteristics of flow separation formation and development. Afterward, different models are compared, and it is found that the BiLSTM with multi-head self-attention model achieved the lowest mean squared error, which is because of its outstanding predicting ability in asymmetric separation cases. The work of this paper indicates that the quasi-3D loss prediction model proposed in this paper will be beneficial to the flow separation structure rapid prediction and cascade loss accurate evaluation in compressor design.

New insight into the traditional model of the coagulation cascade and its regulation: illustrated review of a three-dimensional view

The coagulation process relies on an intricate network of three-dimensional structural interactions and subtle biological regulations. In the present review, we illustrate the state of the art of the structural biology of the coagulation cascade by surveying the Protein Data Bank and the EBI AlphaFold databases. Investigations performed in the last decade have provided structural information on essentially all players involved in the process. Indeed, the initial characterization of specific and rather canonical domains has been progressively extended to complicated multidomain proteins. Recently, the application of cryogenic electron microscopy techniques has unraveled the structural features of highly complex coagulation factors, which has led to enhanced understanding. This review initially focuses on the structure of the individual factors as a function of their involvement in intrinsic, extrinsic, and common pathways. A specific emphasis is given to what is known or unknown on the structural basis of each step of the cascade. Available data providing clues on the structural recognition of the factors involved in the functional partnerships of the pathways are illustrated. Recent structures of important complexes formed by these proteins with regulators are described, focusing on the drugs used as anticoagulants and on their reversal agents. Finally, we highlight the different roles that innovative biomolecules such as aptamers may have in the regulation of the cascade.

Charge exchange processes during carbon ion fragmentation at an energy of 300 MeV/nucleon: comparison with models of ion-ion interactions

Experimental data are presented on the differential cross sections to produce 11Be, 12Be, and 12B isotopes at the carbon beam fragmentation with an energy of 300 MeV/nucleon in the FRAGM experiment conducted based on the ITEP-TWAС multipurpose accelerator complex. The obtained experimental data are compared with the predictions of the models of ion-ion interactions: binary cascade and quantum molecular dynamics model.

Chinese parents' marital and coparenting relationships, parental differential treatment of siblings, and adolescents' sibling relationships.

Parents' differential treatment of siblings has been understudied among sibling research in Chinese societies where the government ended its one-child policy. This study, using a three-wave longitudinal design, explored the associations among parents' marital and coparenting relationships, parental differential treatment of siblings, and adolescents' sibling relationships within a theoretical framework of a developmental cascade model of family influence. Adolescents (Mage = 12.88 years, SD = 0.76 at Time 1; 51.2% girls) and their mothers from 260 families in China participated in this research (first data collection in January 2018). Mothers completed questionnaires that assessed marital and coparenting relationship quality, and adolescents completed questionnaires that assessed their perceptions of parental differential treatment as well as sibling intimacy and conflict. The results, based on path analyses, revealed that marital satisfaction at Time 1 was positively associated with coparenting support at Time 2. Coparenting support at Time 2 was negatively associated with adolescents' perception of parental differential treatment at Time 3. Parental differential treatment at Time 3 was negatively associated with sibling intimacy at Time 3 but positively associated with sibling conflict at Time 3. These results are consistent with the developmental cascade hypothesis in that parent-parent subsystems influenced parental differential treatment of siblings as a parent-child subsystem, which in turn spilled over to create negative family processes that exacerbated adolescents' sibling subsystem quality. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

ИСПОЛЬЗОВАНИЕ ПРОЕКТНОГО ПОДХОДА ПРИ ВЫБОРЕ МОДЕЛИ УПРАВЛЕНИЯ ЖИЗНЕННЫМ ЦИКЛОМ НАУЧНО-ИССЛЕДОВАТЕЛЬСКОЙ РАБОТЫ СТУДЕНТОВ В ВЫСШЕМ ФАРМАЦЕВТИЧЕСКОМ ОБРАЗОВАНИИ

One of the main directions of the educational policy of higher pharmaceutical education is the formation of a graduate researcher to create advanced technologies in the field of life sciences. The optimal conceptual basis for the management of students' research work is the project approach. The purpose is to study the possibilities of using the project approach of the RMVOK–6 PMI methodology when choosing a model for managing the life cycle of research work (R&amp;D) of students in higher pharmaceutical education. Materials and methods. The models under study are rigid models (cascade, iterative, V-model) and flexible models (Agile models, AZ model). The assessment was carried out by 19 experts in the fields of knowledge of the "Six-pointed star" (SPS) PMBOK model according to the Likert scale. Results and discussion. It is established that rigid and flexible models can be selectively applied in the research of junior and senior students, depending on the priority of individual areas of knowledge of the SPS PMBOK model and on the scale of work. Among rigid models, the iterative model and the V-model were better than the cascade model in some areas of knowledge and at a certain scale of research. Among flexible models, Agile models outperformed the AZ model in terms of "time" and "cost", and the AZ model was better than Agile models in terms of "quality" and "risk". Conclusion. The preferential opportunities for the application of project lifecycle management models in the research of students in each of the fields of knowledge of the SPS PMBOK model are shown. For a more detailed study of the process of choosing a project lifecycle management model for research students, it is necessary to create an algorithm for this process.

The cascade influence of grain trade shocks on countries in the context of the Russia-Ukraine conflict

The Russia-Ukraine conflict has severely impacted global food security. This may increase the risk of supply chain disruption in low-income countries that rely heavily on grain imports. This study used production and trade data for wheat, barley and maize from 1995 to 2021 to construct longitudinal trade networks. On this basis, a cascading failure network model of shock propagation was used to identify the direct or indirect dependence of other countries on grain exported from Russia and Ukraine and the impact caused by trade shocks. The results revealed that the interruption of grain exports from Russia and Ukraine has resulted in an increasing impact on the global grain trade year by year and that the wheat trade is the most vulnerable to shock propagation, but it is also the most resilient. Russia and Ukraine interrupt exports of grain, causing more than 50% reduction in direct imports to 30 countries, including Eritrea, Seychelles, Kazakhstan and Mongolia. A shock propagation model that considers indirect dependence yields divergent results, with lower middle income (LM) countries in North Africa, Southeast Asia and West Asia facing supply shocks from reduced imports because they are unable to fully exploit the trade channels to balance grain supply and demand. Under the COVID-19 pandemic, this indirect dependence on imports is more prominent. It is worth noting that Eastern and Southern European countries often act as intermediaries to spread shocks during cascading failures. In the process of shock propagation, the main suppliers of grain include the United States, Canada, France, Argentina and Brazil. After the outbreak of COVID-19, the import demand faced by Australia increased significantly. We also examined how nodal characteristics relate to shock propagation dynamics and country vulnerability, finding that high import diversity, low import dependence and regional characteristics are effective in buffering countries from supply shocks. This study contributes to our understanding of the external supply risks for grain arising from the Russia-Ukraine conflict in a pandemic context, highlights the issue of accessibility in food security and provides trade policy recommendations to mitigate national vulnerability to food insecurity, thereby creating a resilient food trade system.

Identification of influential nodes with Shapley Influence Maximization Extremal Optimization algorithm

The Influence Maximization Problem is a challenging computational task with multiple real-world applications. A new approach to this problem based on cooperative game theory and optimization called the Shapley Influence Maximization Extremal Optimization approach is proposed. The influence maximization problem for the independent cascade model is considered as a cooperative game, where players seek to choose seeder nodes to maximize the size of the influence set of their cascade model by maximizing their average marginal contribution to all possible coalitions. Numerical experiments conducted on both synthetic and real-world networks and comparisons with state-of-the-art algorithms show the potential of the proposed approach.

Dynamic Evaluation Method for Urban Road Network Resilience Based on Congestion Propagation and Dissipation

Urban road networks are often affected by natural disasters, such as rainstorms, with consequences spreading from partial failures to massive network-wide disruptions. The pattern of road network resilience is grasped by capturing the changes in the failure process of the road network cascade. This research presents a method for dynamic evaluation of road network resilience, with extreme rainstorm weather as the background. The fundamental diagram of traffic flow is adopted into the shock wave model to obtain the congestion propagation and dissipation model with speed as input quantity. Then, it is networked and applied to the road network to develop a cascading failure model. The model can be used to identify critical nodes, combining the Monte Carlo method and Dijkstra algorithm, as well as to construct time-varying evolution scenarios of road network resilience when critical nodes are broken. Furthermore, VA (the variations of all OD pairs in the road network) and RI (the resilience indicators of road network) indexes for dynamic evaluation of road network resilience are developed, that visualize the resilience of the road network for a specific region, a specific event, and a specific time. In the case study, the proposed method is employed to evaluate the resilience of the urban road network under a real case of the Zhengzhou 720 rainstorm in China, and its practicality is verified.