Dynamic Bayesian Network Research Articles

Dynamic Bayesian Networks (DBNs) are probabilistic models with a directional structure employed to model temporal processes. Three approaches to DBN structure learning are constraint-based, score-based, and hybrid. The score criterion determined in the score-based and hybrid approach has a certain effect on structure learning and this study aims to examine their performance by diversifying the score criteria used in DBN structure learning in addition to the scores commonly used in the literature. Thus, the Akaike-based information criteria as Akaike Information Criterion (AIC), Consistent AIC (CAIC), Kullback-Leibler Information Criterion (KIC), AIC4, and the Bayesian-based information criteria as Bayesian Information Criterion (BIC), Adjusted BIC (BICadj), Haughton BIC (HBIC), BICQ were adapted into the DBN structure learning. The obtained results were discussed.

Temporal and dynamic Bayesian networks for prognosis and diagnosis in clinical settings: A scoping review.

Managing risks for urban sustainable development: A multidimensional SDG11 assessment based on dynamic Bayesian networks

Developing a probabilistic risk assessment framework for construction projects based on Dynamic Bayesian Network

Fatigue crack prediction based on distributed optical fiber sensing data and dynamic Bayesian network

Fast nuclide identification method based on hybrid dynamic Bayesian network.

An interpretable dynamic Bayesian network method for time-varying seismic liquefaction uplift risk assessment of underground rectangular structures

Background: Current fMRI (functional magnetic resonance imaging)-driven brain information decoding for visual image reconstruction techniques faces issues such as poor structural fidelity, inadequate model generalization, and unnatural visual image reconstruction in complex scenarios. Methods: To address these challenges, this study proposes a Dynamic Confidence Bayesian Adaptive Network (DCBAN). In this network model, deep nested Singular Value Decomposition is introduced to embed low-rank constraints into the deep learning model layers for fine-grained feature extraction, thus improving structural fidelity. The proposed Bayesian Adaptive Fractional Ridge Regression module, based on singular value space, dynamically adjusts the regularization parameters, significantly enhancing the decoder’s generalization ability under complex stimulus conditions. The constructed Dynamic Confidence Adaptive Diffusion Model module incorporates a confidence network and time decay strategy, dynamically adjusting the semantic injection strength during the generation phase, further enhancing the details and naturalness of the generated images. Results: The proposed DCBAN method is applied to the NSD, outperforming state-of-the-art methods by 8.41%, 0.6%, and 4.8% in PixCorr (0.361), Incep (96.0%), and CLIP (97.8%), respectively, achieving the current best performance in both structural and semantic fMRI visual image reconstruction. Conclusions: The DCBAN proposed in this thesis offers a novel solution for reconstructing visual images from fMRI signals, significantly enhancing the robustness and generative quality of the reconstructed images.

Mental health and academic success are increasingly interdependent challenges for university students worldwide. This study developed and validated dynamic Bayesian models to predict academic performance and psychological risk across semesters using probabilistic approaches. We analyzed a cohort of 3,276 undergraduates and externally validated findings against an independent cohort of 5,112 students. Dynamic Bayesian Networks (DBN) and Bayesian Networks (BN) were trained using psychological scores (PHQ-9, GAD-7, PSS-10, CD-RISC) to model psychological risk and academic records to model academic outcomes. Ten-fold temporal cross-validation was conducted internally, and comparative analyses involved Random Forests, XGBoost, Deep Neural Networks, and TabTransformer models. DeLong’s tests compared AUCs and permutation tests assessed Brier scores. Internally, BN achieved 91.0% accuracy, an AUC of 0.84 (95% CI 0.81–0.87), and a Brier score of 0.128, while DBN achieved 94.2% accuracy, an AUC of 0.86 (95% CI 0.84–0.89), and a Brier score of 0.124. In external validation, BN achieved 90.0% accuracy and an AUC of 0.88 (95% CI 0.85–0.90), and DBN achieved 92.0% accuracy and an AUC of 0.91 (95% CI 0.88–0.93). Top predictors included GPA, stress scores, depression scores, and intervention engagement. Posterior predictive p-values exceeded 0.44 across GPA and both outcome domains, indicating adequate calibration. Dynamic Bayesian modeling enables accurate, uncertainty-resilient prediction of both psychological risk and academic outcomes among university students.

Prediction of electricity price intervals using dynamic bayesian networks

Aiming at the problem that existing equipment importance evaluation methods fail to consider interconnectivity between pieces of equipment, variability after maintenance, and the impact of dynamically changing situations on importance, and focusing on the dynamic support needs of equipment in a conflict environment, this paper proposes a batch allocation method for equipment maintenance tasks considering dynamic importance. The purpose of this study is to determine the batch priority of equipment maintenance based on the dynamically changing importance of pieces of equipment. First, a dynamic importance index system is constructed: a real-time CRITIC-AHP combined weighting method is used to calculate team importance, a dynamic Bayesian network (DBN)-influenced method is used to calculate relative importance, an attention–LSTM time-series prediction method is used to calculate future importance, and then a dynamic entropy weight method is adopted to objectively integrate the three types of importance. Second, a dual-objective optimization model with the maximum equipment importance and the minimum total maintenance time is built, with mobile distance, maintenance time, and maintenance capacity as constraints. The Dynamic Particle Swarm Optimization (DPSO) algorithm is used to solve this model, and its dynamic adaptability is improved through environmental change detection and adaptive adjustment of inertia weight. Finally, the batch allocation of maintenance tasks is realized. Example verification shows that compared with the expert scoring method, the errors of the three importance calculation methods are all reduced by more than 60%, the optimization speed of the dynamic PSO algorithm is 47% faster than that of the static algorithm, and the constructed model has good stability. This method can provide a reference for maintenance support command decisions.

IntroductionUrban communities, as the basic unit of urban governance, play a crucial role in responding to public health emergencies (PHEs). This study aims to investigate the resilience measurement and optimization strategies of urban communities in responding to PHEs in order to improve their resilience.MethodsThe study constructed a resilience assessment framework and identified 31 key influencing factors to measure the resilience of case communities in Nanjing. Through sensitivity analysis, static optimization strategies were proposed from social, environmental, and economic levels. Dynamic Bayesian network inference simulation and importance analysis were used to propose dynamic optimization strategies from pre, during, and long-term perspectives.ResultsThrough the combination of dynamic and static strategies, community managers promote resilience building from both short-term and long-term perspectives.DiscussionThe study provides a valuable reference for comprehensively improving the emergency management system.

Existing studies have examined various measures to mitigate the ripple effect of supply chain disruptions, but few have focused on the optimal timing for supplier replacement, particularly in cruise supply chains. This study proposes a novel analytical framework that, for the first time, integrates causal dynamic Bayesian networks, Do-calculus, and mathematical programming to assess supplier replacement timing for controlling disruption propagation. First, a supply chain disruption ripple effect model is constructed: a dynamic Bayesian network captures the ripple effect without supplier replacement, while the causal dynamic Bayesian network and Do-calculus capture the ripple effect under supplier replacement. Building on this model, three models of supply chain risk, service level, and cost are developed. Then, a multi-objective non-convex mixed-integer programming model is formulated to determine the optimal supplier replacement timing, aiming to minimise risk, maximise service level, and minimise cost. Finally, the empirical analysis of cruise supply chain operations shows that risk threshold settings influence the timing and frequency of supplier replacement. This leads to a nonlinear relationship among cost, risk, and service level. Specifically, this relationship manifests as phased improvements, temporary fluctuations due to increased strategic complexity, and diminishing marginal returns as cost inputs rise.

The electronic control system assumes a crucial role in the management, regulation, and safeguarding of equipment within subsea production systems. To ensure the normal operation of the system and improve the reliability of the electronic control system, hardware redundancy is often employed. External shocks can accelerate the state transition of components, and the state changes of electronic control system components may affect other redundant components. Therefore, this paper proposes a multi-state competing failure reliability assessment method based on the correlation of redundant components. By analyzing the degradation and sudden failure processes of electronic control system components, a system state transition probability matrix based on Markov processes is constructed to delineate component state categorization and evolution. The hierarchical model is combined with dynamic Bayesian networks to quantify the impact of system redundancy component correlation on model parameters using a set of hyperparameters. Building upon this analysis, a hierarchical dynamic Bayesian network grounded in competing failure process is established. The probability of each component being in different states after undergoing a competing failure process is analyzed. After considering the influence of redundant component correlation, the overall system reliability deteriorates more rapidly. Finally, the system state evolution under different maintenance strategies is analyzed.

In recent years, there has been an increase in the frequency of severe weather events (like hurricanes). These events are responsible for most power outages in power distribution systems (PDSs). Particularly susceptible to storms are overhead PDSs. In this study, the dynamic Bayesian network (DBN)-based failure model was developed for different hurricane scenarios to predict the line failure of overhead lines. Based on the outcomes of the DBN model, a service restoration model was formulated to maximize restored loads and minimize power losses using Particle Swarm Optimization (PSO)-based distributed generation (DG) integration and system reconfiguration. Three different case studies based on the IEEE 33 bus system were conducted. The overhead line failure prediction and service restoration model findings were further used to calculate resilience metrics. With reconfiguration the load restored from 90.3% to 100% for Case 1 and from 34.994% to 80.35% for Case 2. However, for Case 3, reconfiguration alone was not sufficient to show any improvement in performance. On the other hand, DG integration successfully restored load to 100% in all three cases. These results demonstrated that the combined DBN-based failure modeling and PSO-driven optimal restoration strategy under hurricane-induced disruptions can effectively strengthen system resilience.

Identifying suitable areas for ecosystem services (ES) development is essential for balancing economic growth with environmental sustainability in ecologically fragile regions. However, existing studies often neglect integrating future climate and socioeconomic drivers into ES optimization, hindering the design of robust strategies for sustainable resource management. In this study, we propose a novel framework integrating the System Dynamics (SD) model, the Patch-based Land Use Simulation (PLUS) model, the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, and the Dynamic Bayesian Network (DBN) to optimize ES patterns in the Sanjiangyuan region under three climate scenarios (SSP126, SSP245, and SSP585) from 2030 to 2060. Our results show the following: (1) Ecological land (forest) expanded by 0.86% under SSP126, but declined by 11.54% under SSP585 due to unsustainable land use intensification. (2) SSP126 emerged as the optimal scenario for ES sustainability, increasing carbon storage and sequestration, habitat quality, and water conservation by 3.2%, 1%, and 1.4%, respectively, compared to SSP585. (3) The central part of the Sanjiangyuan region, characterized by gentle topography and adequate rainfall, was identified as a priority zone for ES development. This study provides a transferable framework for aligning ecological conservation with low-carbon transitions in global biodiversity hotspots.

Evolutionary safety evaluation of a truss bridge using dynamic Bayesian networks

Quantitative risk assessment of cruise ship turbochargers using type-2 fuzzy-FMECA and dynamic Bayesian network approach

Genetic signatures predict social-cognitive trajectories in ultra-high-risk psychosis: A 24-month longitudinal study.

Vehicle localization in an explainable dynamic Bayesian network framework for self-aware agents