Search Results (695)

As an essential component of dynamic loads, traditional time-domain identification methods exhibit notably insufficient accuracy when dealing with distributed dynamic load identification under unknown initial conditions. This paper explores a novel and effective methodology, utilizing the Bayesian framework and orthogonal polynomials fitting, to reconstruct the distributed dynamic loads of thin plate structures over any arbitrary time period under unknown initial conditions. The forced vibration under the orthogonal basis function loads and the free decay vibration after the removal of basis function loads are used to characterize the forced vibration induced by the identified distributed dynamic load and the decay vibration caused by unknown initial conditions, respectively. By integrating structural dynamic responses within a multi-layer Bayesian framework, the time history and spatial distribution of the load over any arbitrary time period are identified. The innovation of this methodology is that the contribution of the initial conditions to the response is independently characterized by the free decay response caused by the removal of the basis function loads, which effectively resolves the issue of insufficient identification accuracy in existing traditional time-domain methods due to unknown initial conditions. Consequently, the accuracy and reliability of the distributed dynamic load identification is significantly enhanced, which provides a new solution for distributed dynamic load identification under unknown initial conditions. Additionally, simulation cases involving various load conditions and noise levels are discussed under unknown initial conditions over arbitrary time periods. The results demonstrate that the proposed method achieves favorable identification accuracy and robustness under unknown initial conditions. Full article

The quality and safety supervision of agricultural products at the grassroots level has long faced the triple superposition dilemma of small-sample sampling, multi-source evidence conflict, and risk chain evolution. Although existing data-driven models have considerable accuracy, they are difficult to leverage for intervention decisions, and the simple serial connection of traditional Bayesian networks and evidence theory cannot respond to dynamic scenarios. Aiming at this research gap, this paper constructs a dynamic risk assessment model, CIBE-DR, that deeply couples Bayesian networks with evidential reasoning. It contains three core innovations. First, the structure learning method of the causally identifiable Bayesian network embeds a graded do-calculus identifiability score covering both back-door and front-door criteria into the BDeu scoring function and combines this reward with an expert-prior divergence penalty that breaks Markov equivalence so as to realize the transition from relevance modeling to intervention decision modeling. Second, the conflict-aware adaptive evidence synthesis rule orthogonally decomposes multi-source conflict into an epistemic component and an ontological component, which are modeled respectively by Tsallis belief entropy and abductive inference over a discrete twenty-seven-point heterogeneity hypothesis space and are then fused under a reparameterized Dempster–Yager interpolation in which the two endpoints recover the two named rules under a single consistent interpretation. Third, the bidirectional closed-loop coupling mechanism between BN and ER realizes the mutual calibration between the conditional probability table and the evidence credibility prior under a Lyapunov monotone descent argument with the explicit Lipschitz bound $L_{\theta }$ ≤ 0.028 < 1, endowing the model with time-varying self-correction ability. Based on experiments on 156,847 sampling samples from counties and townships in East China, Central China, and Southwest China from 2021 to 2024, the proposed method achieved the best value in six of the seven evaluation indicators, with a minority recall of 0.864 ± 0.014, an intervention effect estimation error of 0.063 ± 0.005, and a dynamic response delay of 2.8 ± 0.3 days, significantly ahead of eleven mainstream baselines under the McNemar test on classification ($p$ < 0.001) and the Wilcoxon signed-rank test on intervention-effect estimation ($p$ < 0.001). The only indicator on which CIBE-DR does not lead is overall accuracy, which is 0.002 lower than that of Transformer; this difference does not reach statistical significance under the McNemar test ($p$ = 0.32) and does not weaken the value of grassroots supervision in the strong-imbalance scenario where the positive rate is only 1.04%. The robustness advantage of the model is particularly prominent in the scenarios of sparse data, adversarial perturbation, and prior-graph incompleteness, and the intervention-effect estimates were additionally validated against two post-2022 policy interventions with absolute deviations of 1.4 and 1.2 percentage points respectively. These results verify the product gain and grassroots deployability of the three mechanisms. Full article

Competing-risks models play an important role in reliability and survival analysis because failures often arise from several latent causes acting simultaneously. In this paper, we study a two-cause independent competing-risks model in which the latent lifetimes follow the generalised exponential distribution under the improved adaptive Type-II progressive censoring scheme. The proposed framework aims to estimate the model parameters together with the reliability and hazard-rate functions using both classical and Bayesian inference. The frequentist analysis develops maximum likelihood estimators and approximate confidence intervals based on asymptotic theory, whereas the Bayesian analysis employs independent Gamma priors, squared-error loss, and a Metropolis–Hastings algorithm to obtain posterior estimates and highest posterior density credible intervals. An extensive Monte Carlo simulation study is conducted to investigate their finite-sample performance under different censoring schemes, threshold settings, and sample sizes. Finally, two real competing-risks datasets are analysed to illustrate the practical applicability of the proposed methodology and to demonstrate that the generalised exponential competing-risks model provides a competitive alternative for reliability and survival data analysis. Full article

To address the challenges in risk prevention and control of post-earthquake gas explosions in high-rise buildings and the deficiencies of traditional methods in handling uncertainty, this paper conducts a risk evolution analysis from the perspectives of fuzzy Bayesian networks (FBNs) and complex network (CN) theory. First, based on comprehensive risk factor identification, an earthquake-gas explosion disaster chain evolution model was constructed. Subsequently, the nodes and logical relationships of the disaster chain were mapped through Bayesian network (BN) topology, with fuzzy set theory employed to determine prior and conditional probability parameters for causal reasoning and risk diagnosis. Finally, complex network (CN) centrality metrics were introduced to quantify node topological importance, and chain-cutting disaster mitigation strategies were proposed accordingly. The research results indicate that gas overrun (M2) is the node with the highest comprehensive importance, while sensitivity analysis further confirms that it remains the most critical controllable node for interrupting the disaster chain. This method effectively reveals the disaster evolution mechanism and provides a scientific reference for disaster prevention and mitigation decision-making in high-rise buildings. Full article

In distribution networks with a high resistance-to-reactance (R/X) ratio, grid-forming (GFM) inverters can adopt reverse droop control (P-V, Q-f) to achieve the decoupling of active and reactive power. When grid voltage sags trigger the overcurrent protection of the inverter, the interaction between the circular current limiter and the embedded virtual impedance leads to highly complex nonlinear large-signal dynamics. Reverse droop control drives the system power angle via reactive power, which renders the conventional transient stability analysis method based on the P-δ curve invalid, making it difficult to analyze the transient stability of converters based on reverse droop control. To address these issues, this paper first establishes an equivalent circuit model of a GFM inverter considering the circular current limiter and virtual impedance. Second, a transient stability analysis method based on the Q-δ curve is proposed, and the conditions for the existence of the system’s transient equilibrium point (TEP) and the influence of virtual impedance parameters on it are analytically derived. Subsequently, a parameter tuning strategy based on Bayesian optimization (BO) is proposed. Finally, simulation results verify the accuracy of the proposed theory. Full article

In this paper, we develop a unified objective Bayesian framework for inference on the differential effect in the unequal-variance two-sample normal model. Although general theories of objective priors are well established, the higher-order matching properties of priors for this specific parameter have not been fully characterized. Using a model-specific orthogonal parametrization, we derive reference priors and first- and second-order probability matching priors. The main methodological contribution is to show that the proposed second-order matching prior simultaneously satisfies posterior-quantile, alternative-coverage, highest posterior density, cumulative distribution function, and conditional-likelihood-ratio matching criteria. In contrast, the reference priors considered in this study satisfy only the first-order matching criterion. We also establish general conditions for posterior propriety under a broad class of noninformative priors. Simulation studies show that the proposed second-order matching prior generally provides frequentist coverage closer to the nominal levels than the reference priors, including in small-sample and unequal-variance settings. These results provide a theoretically justified and practically useful default prior for objective Bayesian inference on differential effects. Full article

To address the challenges in risk assessment of complex emergency systems, such as difficulties in closed-loop structure modeling, insufficient quantification of dynamic evolution, and poor adaptability to multiple scenarios, this study proposes a dynamic risk assessment method that integrates the System-Theoretic Accident Model and Processes (STAMP) and the Cascading Failure Propagation Model (CFPM). The novelty of this coupling lies in a bidirectional “qualitative diagnosis → quantitative prediction” logic: STAMP’s identification of Unsafe Control Actions (UCAs) provides a theory-grounded blueprint for configuring the CFPM network topology and propagation parameters, while CFPM’s dynamic simulation translates these qualitative control flaws into computable risk evolution trajectories. The proposed framework adopts a two-layer structure of “qualitative modeling–quantitative analysis”. STAMP is used to construct a hierarchical control structure, identify Unsafe Control Actions (UCAs), and analyze the nonlinear interaction mechanisms among “human–organization–technology” factors. For typical scenarios of “fault not processed” and “online fault processing”, CFPM is employed to abstract the system into a node network, quantify the time-step propagation process of node failure probability, calculate the system residual performance index, and generate real-time risk evolution curves. A case study of the Tianjin Port ‘8·12’ explosion accident demonstrates that this method effectively captures the closed-loop interaction characteristics and dynamic risk evolution patterns of emergency systems. Quantitative results reveal a distinct contrast between the two handling scenarios: in the absence of maintenance intervention, system residual performance deteriorates exponentially and rapidly approaches a critical threshold; in contrast, effective online maintenance significantly retards risk accumulation and facilitates gradual system recovery, thereby preventing further escalation of consequences. Compared to traditional methods like Bayesian Networks, it shows stronger applicability by explicitly modeling closed-loop feedback structures and enabling discrete time-step quantification of risk accumulation, and can accurately identify control flaws and quantify risk accumulation effects, thereby providing support for optimizing emergency strategies. Future research should focus on enhancing the method’s adaptability to data uncertainty and cybersecurity threats. Full article

Traditional modular pipelines in autonomous driving (AD) frequently suffer from error accumulation and delayed responsiveness during safety-critical events. Although Embodied Intelligence (EI) introduces a paradigm shift through internal “World Models” for proactive risk mitigation, a substantial gap remains between high-level cognitive theories and real-time, safety-certified deployment. This paper bridges that gap by proposing an Embodied System Integration Methodology (ESIM), which translates cognitive models into fielded robotic systems. Grounded in a “Perception-Imagination-Execution” (PIE) cognitive architecture, ESIM treats risk prediction as an uncertainty-driven, counterfactual closed-loop sensorimotor process. Unlike passive prediction models, the framework employs a Bayesian uncertainty-gated mechanism that selectively triggers a World Model to simulate future risk scenarios only when perceptual degradation occurs. We validate this methodology through a multi-paradigm study spanning three distinct levels: an academic prototype on edge computing platforms, an industrial implementation adhering to ASIL-D (Automotive Safety Integrity Level D) constraints, and an open-source simulation platform. The results demonstrate that by applying hardware acceleration and asynchronous pipelines, the ESIM framework consistently maintains end-to-end latencies within 10–20 ms across heterogeneous hardware. We explicitly address the engineering trade-offs in latency, hardware heterogeneity, and optimization, and establish mathematically grounded probabilistic safety boundaries for black-box neural architectures. Finally, we discuss the framework’s scalability in extreme scenarios, coupling with SLAM pipelines, privacy-preserving federated learning, and generalization potential in the low-altitude economy. Full article

This study develops an integrated risk assessment framework to trace the evolution from multi-factor coupling to systemic failure, using coal mine rock burst as a case study. First, a fault tree containing 56 basic events is established from statistical analysis of accident cases from 2010 to 2024. Expert judgment is then combined with fuzzy theory to assign probabilities to basic events, which are further analyzed through a Bayesian Network. Next, differentiated importance measures, including Birnbaum Importance and Fussell–Vesely Importance, are calculated at multiple levels, and gray relational analysis is used to identify the most critical basic events. Results show that management-related factors, particularly insufficient monitoring and inadequate hazard identification, play dominant roles in risk propagation. The Bow-Tie model is subsequently applied to examine inadequate hazard identification in greater depth and to propose targeted preventive measures. Finally, by integrating the comprehensive accident model with chaos theory across the four dimensions of human, machine, environment, and management, the study reveals the internal mechanism of disaster evolution under multi-factor coupling. Validation against objective data confirms the reliability of both probability assignment and critical-event identification. Full article

Capacitive micromachined ultrasonic transducers (CMUTs) have been extensively investigated for applications in medical imaging and industrial non-destructive testing. However, their relatively low acoustic pressure output remains a major limitation to broader adoption. This paper proposes a CMUT structure that combines collapse-mode operation with a trenched membrane to enhance output performance. An analytical model based on von Kármán large-deflection plate theory is developed to estimate the optimal radial position of the trench, thereby defining the search space for subsequent Bayesian optimization. Single-parameter sequential Bayesian optimizations are first performed to identify the individual effects and optimal ranges of the trench’s radial position, depth, and width. Subsequently, a three-parameter global Bayesian optimization framework is employed for global parameter refinement. The three-parameter joint optimization reveals strong synergistic interactions among the design variables, achieving a higher output pressure of 72.34 kPa compared to 70.02 kPa from sequential approaches. Under identical operating conditions, the optimized trenched membrane CMUT exhibits a 100.15% increase in output acoustic pressure and a 28.77% improvement in the pressure-bandwidth product compared to a uniform membrane. Statistical analysis across multiple independent runs yielded a coefficient of variation (CV) of only 0.052% for the output pressure in the three-parameter global optimization results. This confirms that the proposed framework robustly optimizes CMUT designs for high output pressure, offering a promising technical approach to enhancing device performance. Full article

Contemporary cyber threats increasingly employ multi-stage and behaviourally adaptive strategies that challenge static intrusion detection and non-adaptive deception mechanisms. Existing approaches typically treat threat detection, deception deployment, and adversarial attribution as separate functions, limiting timely response and underusing the behavioural evidence generated during attacker interaction. This study develops and evaluates a theory-informed computational and operational framework for autonomous cyber deception. The principal research artefact is a reusable closed-loop architecture rather than a single predictive model: it specifies the interacting components, interfaces, data and control flows, decision rules, and feedback mechanisms that connect detection, deception, telemetry, and attribution. Methodologically, the study follows an engineering design-and-evaluation approach comprising problem and requirement identification from the literature, architectural synthesis, component-level mathematical modelling, prototype implementation, and controlled cyber-range evaluation. In this context, modelling refers to the distinct computational models embedded within the framework: a hybrid detection model combining supervised classification, anomaly detection, and temporal sequence analysis; a Markov Decision Process and reinforcement-learning policy model for selecting and reconfiguring deception actions under engagement, intelligence-gain, resource, and containment objectives; and similarity-based and Bayesian attribution models for estimating MITRE ATT&CK techniques from incomplete behavioural evidence. The component models were developed offline using the NSL-KDD, CICIDS2017, UNSW-NB15, and ToN-IoT datasets, while the integrated prototype was evaluated separately in a controlled enterprise-like cyber range using reconnaissance, brute-force, exploitation, and multi-stage attack scenarios. The reported classification metrics were calculated from the labelled cyber-range evaluation events, not by pooling the four benchmark datasets. On this integrated cyber-range evaluation set, the system achieved 95.4% detection accuracy, 93.6% precision, 94.7% recall, and a 94.1% F1-score, with a mean detection latency of 85 ms. It also achieved 100% honeypot deployment reliability, 92% dynamic reconfiguration success, 88% fingerprinting resistance, and attacker engagement durations of up to 280 s. The attribution component demonstrated end-to-end generation of ATT&CK-aligned technique hypotheses from deception-derived telemetry; however, the present archived evaluation does not support per-technique or baseline-comparative performance claims. These findings show that specialised models and operational services can be coordinated within a unified adaptive defence process, while also identifying the additional class-level and ablation evidence required for rigorous attribution validation. Full article

Modern Bayesian hierarchical methodologies allow us to leverage spatio-temporal dependencies between observations, enhancing both health effect estimation and map visualization in efficient and flexible ways. However, the necessary levels of statistical software are often unavailable or difficult to access. We have recently examined Bayesian spatio-temporal models to estimate the association between COVID-19 death counts and various social and environmental risk factors, including ambient air pollution exposure. Typically, it is very common that in an infection disease mapping problem with count data, we have excessive zeros, and it is usually for over-dispersed count outcome variables. Furthermore, the theory suggests that the excess zeros are generated by a separate process from the count values and that the excess zeros need to be modeled independently. Our proposed models are specially designed to handle the zero-inflation and over-dispersion in count data through Zero-Inflated Negative Binomial regression with random effects that vary across time and space within a Markov Chain Monte Carlo framework. Drawing on our knowledge and experience, we aim to provide a simple, unified, and publicly available software that can be applied in various disease mapping studies under the contemporary Bayesian framework. Full article

Reliable prediction of landslide runout distance and influencing areas is critical for landslide hazard zoning and risk assessment. This paper proposed a rainfall-induced landslide prediction and simulation platform based on the developed interpretable Bayesian optimization-extreme gradient Boosting (BO-XGBoost) model and the continuum–discontinuum element method (CDEM) incorporated with a particle strength softening model. First, based on kinematic prediction theory, several key influencing features were selected from rainfall-induced landslide cases to train the BO-XGBoost model. The nonlinear relationship between runout distance and influencing features was effectively captured by the model, with an R² of 0.858. According to Shapley Additive Explanations (SHAP) analysis, the elevation difference between the rear and front edges was identified as the dominant controlling feature. Then, the particle flow method was introduced into CDEM and a particle strength softening model was proposed to simulate the landslide process. The applicability of the proposed softening model was validated through the case of the Yigong landslide. Additionally, a collaborative application of the BO-XGBoost model and CDEM simulation was implemented for the Wangjiawan landslide. The runout distances predicted by the BO-XGBoost model and CDEM simulation were 256.8 m and 320 m, respectively, corresponding to relative errors of 14.40% and 6.67% compared with the observed value of 300 m. The methodological innovation and application results demonstrate that this paper provides a reliable reference for the assessment and mechanism analysis of landslides. Full article

Advanced Persistent Threats (APTs) pose a serious threat to Internet of Things (IoT) systems because of their stealthiness, persistence, and ability to adapt to defensive responses. Communication-Based Train Control (CBTC) systems, as IoT-enabled railway signaling infrastructures, have evolved from relatively closed operational environments into interconnected cyber-physical networks, exposing train control systems to coupled cyber intrusion and operational-safety risks. To address this challenge, this paper proposes an alert-driven active defense framework for CBTC systems that integrates Bayesian belief updating, hypergame-based cognitive-bias modeling, and Hierarchical Reinforcement Learning (HRL). The framework converts intrusion detection system (IDS) alerts, network traffic observations, and cyber-physical observations into belief-state, transition, and reward inputs. The Bayesian model estimates attacker type and attack stage, the hypergame model represents deception-induced asymmetric cognition between attackers and defenders, and the HRL decouples strategic defense posture selection from tactical defense execution. The scenario-driven simulations in a CBTC APT defense setting show that the proposed model strategy achieves an 87.1% defense success rate against APT attacks while consuming 62.7% of the normalized defense resources, outperforming DQN, PG, and PPO under the same test conditions. These results suggest that explicitly coupling cyber observations, CBTC operational constraints, and hierarchical deception-aware policies can improve cost-aware active defense for railway signaling infrastructures. Full article

Despite widespread adoption of cybersecurity awareness training (CSAT), a persistent knowledge–behaviour gap continues to undermine organisational security posture, particularly in resource-constrained and developing-country contexts. This 12-week quasi-experimental field study evaluated an AI-adaptive CSAT platform against traditional instructor-led training (ILT) across three Yemeni organisations (total N = 187; AI-Adaptive: n = 94; Control: n = 93). The system used a 4-parameter Bayesian Knowledge Tracing (BKT) engine—with interpretable guess and slip signals—as an auditable pedagogical decision layer that triggered Protection Motivation Theory (PMT) and Theory of Planned Behavior (TPB)-aligned interventions. Extreme baseline imbalances (Cohen’s d > 2.0), at which standard ANCOVA residual adjustment alone is known to be biased and which necessitated advanced causal-inference triangulation, were addressed via a four-method protocol (ANCOVA, Propensity Score Matching, Difference-in-Differences, mixed-effects). All four methods converged on consensus effect sizes of d = 0.66–0.89. IT-verified Tier 2–3 incidents declined by 48.9% (incidence-rate ratio [IRR] = 0.51, 95% CI [0.38, 0.68]); blinded phishing click-rates fell from 8.8% to 2.1% (χ²(1) = 8.74, p = 0.003). Bootstrapped mediation analysis (PROCESS Model 4; 5000 draws) indicated that coping self-efficacy and perceived behavioural control—but not threat appraisal—were jointly associated with 66.4% of the total compliance effect. Rosenbaum bounds Γ = 2.1; E-values ≥ 3.4. The findings are consistent with the hypothesis that AI-adaptive cybersecurity training produces robust, theoretically explicable benefits and that the coping-appraisal pathway, not threat salience, is the active psychological mechanism. The four-method triangulation framework offers a replicable standard for field evaluations with non-random assignment. the consensus envelope d = 0.66–0.89 is the observed range of point estimates across the four estimators; per-method 95% CIs are reported below indirect effect via coping self-efficacy = 0.843 [0.52, 1.19], via PBC = 0.524 [0.28, 0.81], via threat appraisal = 0.059 [−0.07, 0.21] (ns); direct effect c’ = 0.63 (p = 0.026); total effect c = 2.06 [1.58, 2.54]. Full article