Critical Nodes Research Articles

Ecological Suitability Evaluation of Chinese Pearleaf Crabapples in Horqin Right Wing Front Banner Based on GIS

The Chinese pearleaf crabapple tree, belonging to the Rosaceae family and with the scientific name Malus asiatica, bears fruits that serve as both food and medicine. The Horqin Right Wing Front Banner is a critical node for ecological security in the northern border regions of China, exerting significant influence on ecological health in China. Additionally, the resources in this region are limited, and residents have historically relied solely on maize cultivation for sustenance. Therefore, local measures combining ecological conservation and economic development have been implemented in vigorously promoting the Chinese pearleaf crabapple industry. Hence, it is necessary to study the ecological suitability of the local Chinese pearleaf crabapple. In this study, 15 evaluation indicators are employed for this purpose, with the analytic hierarchy process (AHP) used to calculate single-factor weights. Additionally, GIS is utilized to establish membership functions for achieving dimensionless evaluation indicators, as well as a weighted index and model used to compute comprehensive suitability scores. Using the natural breakpoint method, the overall scores were categorized into five groups: most suitable, more suitable, barely suitable, unsuitable, and not applicable. The validation of zoning results includes verification of existing planting locations and conducting principal component analysis of the characteristics and yield of Chinese pearleaf crabapple to ensure the accuracy of the zoning. It is found that the validation results are generally consistent with the zoning results. The results indicate that the most suitable, more suitable, suitable, barely suitable, and unsuitable represent 16.49%, 32.07%, 30.36%, 14.52%, and 6.55% of the total zoning area, respectively. These findings can be applied to land use planning and in expanding the planting area of Chinese pearleaf crabapple.

The critical node game

The critical node game

Identification of key nodes in complex networks by using a joint technique of nonnegative matrix factorization and regularization

Identifying key nodes in complex networks is essential to deeply understand and fully utilize the properties and functions of complex systems. Currently, existing traditional methods perform critical nodes identification by manually selecting important attribute features of nodes, but there are limitations in this approach. Manual selection of attribute features may overlook non-obvious features related to nodes criticality and correlations between attribute features. To compensate for the shortcomings of traditional methods, a Joint Technique for identifying critical nodes, called JTNMFR, is presented based on Nonnegative Matrix Factorization and Regularization. Factorization of weighted adjacency matrix is performed to obtain potential attribute features of nodes, and communicability network matrix and similarity matrix are introduced as regularization terms to control sparsity of the decomposition results. Ultimately, the importance of nodes is assessed by constructing an objective function that integrates these two aspects and utilizing alternative iteration to obtain the attribute matrix. To validate the accuracy and reliability of JTNMFR, we compare it with nine other identification approaches on eight real networks. Experimental results show that JTNMFR not only significantly outperforms the other algorithms in terms of accuracy of node importance, monotonicity, and node spreading ability but also provides a more accurate means of assessing node importance.

A Circulation Society, Reconsidered: Syrian Jewish Merchant Networks after the Exodus from Aleppo

This article analyses the durability of transregional Syrian Jewish merchant networks through the twentieth and early twenty-first centuries, when the centre of these trading networks shifted several times in response to economic transformations and political pressures. Migration patterns from Aleppo following the Ottoman collapse and the exodus after 1947 call for a modified conceptualisation of centres, peripheries and circulation from dominant approaches to merchant networks and circulation societies. Centres are generally thought of as the origin points of persons and goods – namely, women, religious specialists and collateral-free credit – which circulate exclusively within the network; peripheries are nodes which merely receive and depend on centres in these respects. I add to this by analysing central or critical nodes as those where different kinds of mobility intersected to inject new vitality into the networks. Peripheries are not only dependent nodes, but vital points of refuge and transition in times of duress. Furthermore, beyond persons and credit, the circulation of aesthetic and ethical standards, in addition to name values, has helped to maintain the integrity of the network in a period of geographic reconfiguration.

Experience-dependent, sexually dimorphic synaptic connectivity defined by sex-specific cadherin expression.

We describe here the molecular mechanisms by which juvenile experience defines patterns of sexually dimorphic synaptic connectivity in the adult nervous system of the nematode C. elegans. We show that starvation of juvenile males disrupts serotonin-dependent activation of the CREB transcription factor in a nociceptive sensory neuron, PHB. CREB acts through a cascade of transcription factors to control expression of an atypical cadherin protein, FMI-1/Flamingo. During postembryonic development, FMI-1/Flamingo has the capacity to promote and maintain synaptic connectivity of the PHB nociceptive sensory to a command interneuron, AVA, in both sexes, but the serotonin transcriptional regulatory cassette antagonizes FMI-1/Flamingo expression in males, thereby establishing sexually dimorphic connectivity between PHB and AVA. A critical regulatory node in this process is the CREB-target LIN-29, a Zn finger transcription factor which integrates four different layers of information - sexual specificity, past feeding status, time and cell-type specificity. Our findings provide the mechanistic details of how an early juvenile experience defines sexually dimorphic synaptic connectivity.

A framework for climate change adaptation of port infrastructures

Adaptation of port infrastructures to climate change and sea level rise effects is highlighted as a key field among transportation systems’ lines of action for adaptation, given their highly exposed location in coastal areas and position as critical nodes in logistic chains and local, regional and national economies. The present work proposes an adaptation assessment framework that, based on a high-resolution compound climate risk assessment, identifies the main threats that climate change may pose to port performance, defines a set of optimized adaptation measures and characterizes constraints for implementation, and finally evaluates the applicability and effectiveness of these measures under diverse climate scenarios and different time frames. The framework is applied in a study case located in the northern coast of Spain. It is shown that the proposed methodology enables port managers and planners to develop tailor-fitted adaptation plans, providing tools to make them coherent with actual and future uncertain climate conditions.

Multilayer analysis of energy networks

Modern interconnected and vulnerable energy infrastructures highlight the critical need for resilience and robustness to maintain the functioning of modern societies amidst growing environmental and operational challenges. This study introduces both multilayer and multiplex network analysis of Belgium’s electricity and gas systems, addressing the complex interdependence inherent in such infrastructure. The resilience is evaluated through random and targeted disruptions, while the robustness is assessed by employing the Molloy–Reed criteria for structural integrity and setting minimum operational thresholds for continuity. Multiplex layer similarities are assessed and subnetworks are identified. The coverage evolution metric quantifies the network’s adaptability under various disruption scenarios. Belgium’s energy infrastructure is used as a case study to uncover critical nodes from different centrality perspectives whose stability is essential for overall network resilience and identify vulnerabilities to disruptions, whether random or targeted. Our insights into the structural robustness, cluster cohesion, and Navigability of energy networks pave the way for more informed decisions in energy infrastructure planning and complex systems management, offering valuable lessons that can be applied globally.

3D-MPEA: A Graph Attention Model-Guided Computational Approach for Annotating Unknown Metabolites in Interactomics via Mass Spectrometry-Focused Multilayer Molecular Networking.

The spectral matching strategy of MS2 fragment spectrograms serves as a ubiquitous method for compound characterization within the matrix. Nevertheless, challenges arise due to the deficiency of distinctions in spectra across instruments caused by coelution peak-derived fragments and incompleteness of the current spectral reference database, leading to dilemma of multidimensional omics annotation. The graph attention model embedded with long short-term memory was proposed as an optimized approach involving integrating similar MS2 spectra into molecular networks according to the isotopic ion peak cluster spacing features to collapse diverse ion species and expand the spectral reference library, which efficiently evaluated the substance capture capacity to 123.1% than classic substance perception tactics. The versatility and utility of the established annotation procedure were showcased in a study on the stimulation of pork mediated by 2,2-bis(4-hydroxyphenyl)propane and enabled the global metabolite annotation from knowns to unknowns at metabolite-lipid-protein level. On the spectra for which in silico extended spectral library search provided a group truth, 83.5-117.1% accuracy surpassed 1.2-14.3% precision after manual validation. β-Ala-His dipeptidase was first evidenced as the critical node related to the transformation of α-helical (36.57 to 35.74%) to random coil (41.53 to 42.36%) mediated by 2,2-bis(4-hydroxyphenyl)propane, ultimately triggering an augment of catalytic performance, inducing a series of oxidative stress, and further intervening in the availability of animal-derived substrates. The integration of ionic fragment feature networks and long short-term memory models allows the effective annotation of recurrent unknowns in organisms and the deciphering of unacquainted matter in multiomics.

Chemogenetic inactivation of the nucleus reuniens and its projections to the orbital cortex produce deficits on discrete measures of behavioral flexibility in the attentional set-shifting task

The nucleus reuniens (RE) of the ventral midline thalamus is a critical node in the communication between the orbitomedial prefrontal cortex (OFC) and the hippocampus (HF). While RE has been shown to directly participate in memory-associated functions through its connections with the medial prefrontal cortex and HF, less is known regarding the role of RE in executive functioning. Here, we examined the involvement of RE and its projections to the orbital cortex (ORB) in attention and behavioral flexibility in male rats using the attentional set shifting task (AST). Rats expressing the hM4Di DREADD receptor in RE were implanted with indwelling cannulas in either RE or the ventromedial ORB to pharmacologically inhibit RE or its projections to the ORB with intracranial infusions of clozapine-N-oxide hydrochloride (CNO). Chemogenetic-induced suppression of RE resulted in impairments in reversal learning and set-shifting. This supports a vital role for RE in behavioral flexibility – or the ability to adapt behavior to changing reward or rule contingencies. Interestingly, CNO suppression of RE projections to the ventromedial ORB produced impairments in rule abstraction – or dissociable effects elicited with direct RE suppression. In summary, the present findings indicate that RE, mediated in part by actions on the ORB, serves a critical role in the flexible use of rules to drive goal directed behavior. The cognitive deficits of various neurological disorders with impaired communication between the HF and OFC, may be partly attributed to alterations of RE -- as an established intermediary between these cortical structures.

From Iya Ijebu’s shop to Igbo market: ethnicity and the transformation of Mushin leather market in Lagos, Nigeria

AbstractLeather markets are critical nodes in the leather industry value chain. They attract manufacturers and small and medium enterprises that facilitate social interactions and opportunities, leading to increased availability of finished leather goods. This study explores the social transformation and organization of Mushin leather market in Lagos megacity based on data collected through observation and in-depth and key informant interviews with traders, association and community members, transporters and customers in the market. Mushin leather market evolved from a shop owned by an indigenous woman known as Iya Ijebu (Ijebu woman) into a leather trading community. State and local governments intervene minimally in the market affairs through sanitation rules and revenue collection. Routes to market socialization include mentoring and apprenticeships, with visible gender disparities. Associational membership guarantees full integration to traders. Strong business, personal and group networks structure ethnic dynamics and occupational specializations. The article reveals the inherent interdependence of social institutions in understanding the organization of the leather market, particularly in terms of how capital, associational norms and networks shape the formation of a heterogeneous market in an indigenous Yoruba community. Mushin leather market is transforming the domains of ethnic trade speciality and patterning intergroup social relations in Lagos megacity.

Effect of Inactivation of Prefrontal Cortex on Sleep-Wake States in Rat

There is a large body of literature to support subcortical regulation of sleep-wake states, but there have been limited studies to investigate the role of cortex in sleep-wake regulation. Previously, we have demonstrated that cholinergic stimulation of prefrontal cortex can reverse general anesthesia and promote wakefulness in unanesthetized rats, and there is emerging evidence to suggest that the prefrontal cortex might be a critical node in arousal state control. To better understand the direct role of prefrontal cortex in the regulation of sleep-wake states, we quantified the effect of prefrontal cortex inactivation, via local tetrodotoxin (156 μM) infusion, on sleep architecture. Under surgical isoflurane anesthesia, adult Sprague Dawley rats (n=7 male) were instrumented with electrodes to record electroencephalogram (EEG) from frontal and parietal cortices, and electromyogram (EMG) from dorsal nuchal muscles. In addition, a bilateral guide cannula was implanted aimed at the medial prefrontal cortex for tetrodotoxin infusion. After at least a week of post-surgical recovery and conditioning to the recording chambers, rats received bilateral microinjection (500 nL) of either 156 μM tetrodotoxin or 0.9% saline (vehicle control). The tetrodotoxin and 0.9% saline infusions were performed 30-minutes before the start of lights-OFF period (8:00 pm) after which EEG and EMG data were recorded for 24h across dark and light cycles. The EEG and EMG data were manually scored (SleepSign, Kissei Comtec Inc.) in 4-second intervals into wakefulness, slow-wave sleep, and rapid eye movement sleep, and then averaged in 3h bins across 24h recording period. A linear mixed model was used to compare the changes in percent time spent in each state, mean duration per episode for each state, and mean number of episodes for each state, between the vehicle control and tetrodotoxin-infusion sessions. Inactivation of prefrontal cortex via tetrodotoxin infusion produced long-lasting statistically significant increase in slow-wave sleep (p<0.05 for 0-3h, 4-6h, 7-9h, 10-12h, 13-15, 16-18, 22-24) and decrease in wakefulness (p<0.05 for 0-3h, 4-6h, 10-12h) and rapid eye movement sleep (p<0.05 for 7-9h, 10-12h, 13-15h). These data further support a causal contribution of prefrontal cortex in regulating arousal states. This work was supported by the National Institutes of Health (Bethesda, MD, USA) grant (R01 GM111293) to GAM and DP, and funding from the Department of Anesthesiology, University of Michigan Medical School, Ann Arbor. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Regulation of IL-1β-mediated inflammatory responses by neural networks

Abstract Sickness behavior associated with inflammation impairs quality of life and increases morbidity and mortality. However, the neural mechanism that mediates inflammation-associated changes are poorly understood. Here, we identify the bed nucleus of the stria terminalis (BNST) as a critical node in mediating responses to cytokines. Administration of recombinant interleukin-1β (rIL-1β) significantly increases serum IL-6 (control vs. rIL-1β; 22.6±4.7 vs 1,487±341.4 pg/ml; p=0.0079). A significant decrease in core body temperature (pre vs. post; 38.1±0.2 vs. 36.1±0.4oC; p<0.0001) and activity level (pre vs. post; 0.2±0.03 vs. 0.03±0.01 A.U.; p=0.0002) is also observed with rIL-1β administration. We visualized rIL-1β-responsive neurons using activity-dependent cell labeling, and identified distinct neuronal populations in the BNST that are active in response to IL-1β. Selective activation of IL-1β-responsive neurons using a chemogenetic approach is sufficient to induce significant increase in serum IL-6 levels (control vs. IL-1β; 26.2±74.1 vs. 272.5±8.3 pg/ml; p=0.01). Reactivation of IL-1β-responsive neurons also induces significant decrease in core body temperature (pre vs. post; 37.6±0.3 vs. 36.1±0.6oC; p=0.02) and activity levels (pre vs. post; 0.1±0.01 vs. 0.06±0.01 A.U.; p=0.02). Our data defines a neural population in the BNST that is sufficient for mediating IL-1β-associated responses, revealing its role in the coordination of inflammatory responses.

Personalised functional imaging-guided multitarget continuous theta burst stimulation for post-stroke aphasia: study protocol for a randomised controlled trial

IntroductionContinuous theta burst stimulation (cTBS), a form of repetitive transcranial magnetic stimulation (rTMS), targeting the language network in the right hemisphere of post-stroke aphasia (PSA) patients shows promising results in...

Enhancing Electrical Network Vulnerability Assessment with Machine Learning and Deep Learning Techniques

This research utilizes advanced machine learning techniques to evaluate node vul- nerability in power grid networks. Utilizing the SciGRID and GridKit datasets, con- sisting of 479, 16,167 nodes and 765, 20,539 edges respectively, the study employs K-nearest neighbor and median imputation methods to address missing data. Cen- trality metrics are integrated into a single comprehensive score for assessing node criticality, categorizing nodes into four centrality levels informative of vulnerability. This categorization informs the use of traditional machine learning (including XG- Boost, SVM, Multilayer Perceptron) and Graph Neural Networks in the analysis. The study not only benchmarks the capabilities of these models in network analy- sis but also explores their potential in identifying critical nodes using features be- yond centrality metrics alone, enhancing their applicability in real-world scenarios. The research addresses a significant gap in effectively assessing the vulnerability of electrical networks, marked by isolated use of traditional centrality metrics and a lack of integration between these combined metrics with both tradiational and ad- vanced machine learning models. The study integrates various centrality measures into a comprehensive metric and advocates for the application of advanced ma- chine learning models, particularly GNNs. It underscores the need for larger and more complex datasets to unlock the full potential of GNNs in network vulnerabil- ity assessments. By comparing the performance of GNN models with traditional machine learning approaches across datasets of different sizes and complexities, the study provides insights into optimizing model selection for network analysis, thereby contributing significantly to the field of network vulnerability assessment.

Optimal PMU Placement to Enhance Observability in Transmission Networks Using ILP and Degree of Centrality

The optimal PMU placement problem is placing the minimum number of PMUs in the network to ensure complete network observability. It is an NP-complete optimization problem. PMU placement based on cost and critical nodes is solved separately in the literature. This paper proposes a novel approach, a degree of centrality in the objective function, to combine the effect of both strategies to place PMUs in the power network optimally. The contingency analysis and the effect of zero-injection buses are solved to ensure the reliability of network monitoring and attain a minimum number of PMUs. Integer linear programming is used on the IEEE 7-bus, IEEE 14-bus, IEEE 30-bus, New England 39-bus, IEEE 57-bus, and IEEE 118-bus systems to solve this problem. The results are evaluated based on two performance measures: the bus observability index (BOI) and the sum of redundancy index (SORI). On comparison, it is found that the proposed methodology has significantly improved results, i.e., a reduced number of PMUs and increased network overall observability (SORI). This methodology is more practical for implementation as it focuses on critical nodes. Along with improvement in the results, the limitations of existing indices are also discussed for future work.

Intelligent construction technology based information sharing network for prefabricated building supply chain: a social network analysis approach

PurposeIn the context of Industry 4.0, intelligent construction technologies (ICT) represented by information technology and networking will undoubtedly provide new impetus to the development of the prefabricated building supply chain (PBSC), but they will also bring various potential risks. So far, there is a large lack of research on the comprehensive consideration of the risks associated with the intelligent transformation of PBSC based on the information sharing perspective, and the critical risks and interactions are still unclear, making it difficult to identify efficient risk mitigation strategies. Therefore, this paper aims to reveal the interactions between stakeholders and clarify the critical risk nodes and interactions in information sharing of PBSC (IS-PBSC), and propose targeted risk mitigation strategies.Design/methodology/approachFirstly, this paper creatively delineates the risks and critical stakeholders of IS-PBSC. Secondly, Data is collected through questionnaires to understand the degree of risks impact. Thirdly, with the help of NetMiner 4 software, social network analysis is conducted and IS-PBSC risk network is established to reveal critical risk nodes and interactions. Finally, further targeted discussion of critical risk nodes, the effectiveness and reasonableness of the risk mitigation strategies are proposed and verified through NetMiner 4 software simulation.FindingsThe results show that the critical risks cover the entire process of information sharing, with the lack of information management norms and other information assurance-related risks accounting for the largest proportion. In addition, the government dominates in risk control, followed by other stakeholders. The implementation of risk mitigation strategies is effective, with the overall network density reduced by 41.15% and network cohesion reduced by 24%.Research limitations/implicationsIn the context of Industry 4.0, ICT represented by information technology and networking will undoubtedly provide new impetus to the development of the PBSC, but they will also bring various potential risks. So far, there is a large lack of research on the comprehensive consideration of the risks associated with the intelligent transformation of PBSC based on the information sharing perspective, and the critical risks and interactions are still unclear, making it difficult to identify efficient risk mitigation strategies.Originality/valueBased on the results of risk network visualization analysis, this paper proposes an ICT-based IS-PBSC mechanism that promotes the development of the integration of ICT and PBSC while safeguarding the benefits of various stakeholders.

Deciphering the similarities and disparities of molecular mechanisms behind respiratory epithelium response to HCoV-229E and SARS-CoV-2 and drug repurposing, a systems biology approach.

Identifying the molecular mechanisms behind SARS-CoV-2 disparities and similarities will help find new treatments. The present study determines networks' shared and non-shared (specific) crucial elements in response to HCoV-229E and SARS-CoV-2 viruses to recommend candidate medications. We retrieved the omics data on respiratory cells infected with HCoV-229E and SARS-CoV-2, constructed PPIN and GRN, and detected clusters and motifs. Using a drug-gene interaction network, we determined the similarities and disparities of mechanisms behind their host response and drug-repurposed. CXCL1, KLHL21, SMAD3, HIF1A, and STAT1 were the shared DEGs between both viruses' protein-protein interaction network (PPIN) and gene regulatory network (GRN). The NPM1 was a specific critical node for HCoV-229E and was a Hub-Bottleneck shared between PPI and GRN in HCoV-229E. The HLA-F, ADCY5, TRIM14, RPF1, and FGA were the seed proteins in subnetworks of the SARS-CoV-2 PPI network, and HSPA1A and RPL26 proteins were the seed in subnetworks of the PPI network of HCOV-229E. TRIM14, STAT2, and HLA-F played the same role for SARS-CoV-2. Top enriched KEGG pathways included cell cycle and proteasome in HCoV-229E and RIG-I-like receptor, Chemokine, Cytokine-cytokine, NOD-like receptor, and TNF signaling pathways in SARS-CoV-2. We suggest some candidate medications for COVID-19 patient lungs, including Noscapine, Isoetharine mesylate, Cycloserine, Ethamsylate, Cetylpyridinium, Tretinoin, Ixazomib, Vorinostat, Venetoclax, Vorinostat, Ixazomib, Venetoclax, and epoetin alfa for further in-vitro and in-vivo investigations. We suggested CXCL1, KLHL21, SMAD3, HIF1A, and STAT1, ADCY5, TRIM14, RPF1, and FGA, STAT2, and HLA-F as critical genes and Cetylpyridinium, Cycloserine, Noscapine, Ethamsylate, Epoetin alfa, Isoetharine mesylate, Ribavirin, and Tretinoin drugs to study further their importance in treating COVID-19 lung complications.

Multi-Constraint and Multi-Policy Path Hopping Active Defense Method Based on SDN

Path hopping serves as an active defense mechanism in network security, yet it encounters challenges like a restricted path switching space, the recurrent use of similar paths and vital nodes, a singular triggering mechanism for path switching, and fixed hopping intervals. This paper introduces an active defense method employing multiple constraints and strategies for path hopping. A depth-first search (DFS) traversal is utilized to compute all possible paths between nodes, thereby broadening the path switching space while simplifying path generation complexity. Subsequently, constraints are imposed on residual bandwidth, selection periods, path similitude, and critical nodes to reduce the likelihood of reusing similar paths and crucial nodes. Moreover, two path switching strategies are formulated based on the weights of residual bandwidth and critical nodes, along with the calculation of path switching periods. This facilitates adaptive switching of path hopping paths and intervals, contingent on the network’s residual bandwidth threshold, in response to diverse attack scenarios. Simulation outcomes illustrate that this method, while maintaining normal communication performance, expands the path switching space effectively, safeguards against eavesdropping and link-flooding attacks, enhances path switching diversity and unpredictability, and fortifies the network’s resilience against malicious attacks.

A new method for unmanned aerial vehicle path planning in complex environments

To solve the problems of UAV path planning, such as low search efficiency, uneven path, and inability to adapt to unknown environments, this paper proposes A double-layer optimization A* and dynamic window method for UAV path planning. Firstly, the neighboring node clip-off rule is defined to optimize the node expansion mode of the A* algorithm, and the obstacle coverage model is designed to dynamically adjust the heurizing function of the A* algorithm to improve the path search efficiency. Then, the Bresenham algorithm is adopted for collision detection and critical path nodes are extracted to significantly reduce the number of path turning points. Secondly, a new tracking index is proposed to optimize the evaluation function of the dynamic window method to make the local path fit the global path further. By detecting the dangerous distance, the dynamic adaptive method of evaluation function weight is designed to improve the fixed weight of the dynamic window method. Finally, the key turning point of optimizing the A* algorithm is taken as the temporary target point to improve the DWA algorithm, and the local part follows the global part, and the fusion of the two algorithms is realized. Simulation results show that the proposed method can significantly improve the efficiency and smoothness of mobile robot path planning, enhance the real-time obstacle avoidance and adaptive ability of unknown environments, and better meet the requirements of complex planning tasks.

A method for safety analysis of human-machine systems based on dynamic Bayesian simulation

Accidents in human-machine systems often lead to serious consequences, so safety analysis is very important for such systems. However, the existing approach to safety analysis of human-machine systems lacks clear delineation of the coupling relationships between human and machine, or provide quantitative analysis. To address these issues, this paper proposes a method for safety analysis of human-machine systems, utilizing dynamic Bayesian network (DBN) and dynamic fault tree (DFT). The core of this method is the identification of human-machine coupling relationships, proposing 10 types of logical relationships and presenting corresponding DFT logic. Then, a conversion method from DFT to DBN is designed to avoid combinatorial explosion in complex FTA calculations. Based on the DBN model, simulation is conducted using Gibbs sampling, which offers higher computational efficiency. Additionally, a method for importance analysis is devised to identify critical nodes that impact the system risk. At last, a case study of refueling mission at space launch site is given to illustrate how to apply the method. Through simulation analysis, the safety risks during the refueling mission are quantitatively assessed, while critical nodes are identified. The results indicate that the dynamic Bayesian simulation method is good in information utilization, dynamic representation, and time performance.