Search Results (529)

We examine whether simulated attack-action evidence can inform a defender that they must commit before an attacker’s type is known. We formulate a five-stage Bayesian Stackelberg security game with five stage-specific actions per player. A Monte Carlo predictor produces type-conditioned action likelihoods on an enterprise graph, while prediction confidence weights the next Bayesian update. The defender strategy is computed by exact follower-response enumeration and linear programming. Evaluation used a simulated 10-node enterprise network, 30 paired trials, bootstrap confidence intervals, and Holm-adjusted Wilcoxon tests. Against a fixed-prior Bayesian Strong Stackelberg Equilibrium, mean gross defense utility increased from 2.141 to 2.197. Mean attack success decreased from 0.691 to 0.686. The paired differences remained significant after multiplicity correction. Outcomes did not differ significantly from an equilibrium updated with coarse reference likelihoods, and the simulation cost reduced net utility by 0.08. Maximum follower regret and constraint violation remained at the specified numerical tolerance. Runtime remained near 0.39 s across networks of 10–100 nodes. An action/type experiment showed rapid growth as follower-response profiles increased. Exact commitment and sequential updating were feasible in the abstraction; simulation was not automatically cost-effective when a usable reference model was available. Full article

In structural engineering, accurate prediction of structural behavior is crucial for ensuring safety and reliability. Traditional parameter estimation methods often rely on deterministic approaches, which may overlook inherent uncertainties in real-world structures. This paper presents a comprehensive manual on utilizing a Bayesian framework to update structural model parameters, offering a robust strategy for quantifying uncertainties and enhancing predictive accuracy. The methodology employs Python, leveraging Open-SeesPy for finite element modeling and PyMC for probabilistic inference. Five distinct examples are provided to illustrate the workflow, ranging from fundamental parameter estimation in structural frames to advanced Hierarchical Stochastic Models (HSMs) for constitutive material calibration. This work serves as a practical guide for structural engineers seeking to adopt novel probabilistic techniques. By integrating Bayesian inference, engineers can effectively account for both measurement noise and intrinsic physical variability, thereby improving the fidelity of predictive models. The use of open-source tools streamlines the implementation process, making these advanced methods accessible to a wider audience in engineering practice. Full article

The flexible and precise design of photovoltaic (PV) skin morphologies for building facades constitutes a critical element for optimizing building energy efficiency and enhancing indoor spatial performance. However, the complex interrelationships among climatic parameters and their spatiotemporal variations pose significant challenges to the prior validity and accuracy of climate-adaptive parametric skin morphology adjustments. To address these limitations, this study proposes a morphological generative framework for climate-adaptive Building-Integrated Photovoltaics (BIPV) facades integrating Artificial Intelligence Algorithms and Bayesian prior-parameterized building envelopes. This framework is specifically designed to facilitate morphological decision-making regarding the overall climate-adaptive opening states of parametric PV skins under spatiotemporal dynamics. The proposed method integrates AI-based pattern recognition in spatiotemporal climate data with Bayesian Network-based prior probability techniques to derive optimal facade morphology schemes with the highest overall climate adaptability scores derived from weather forecasts, thereby achieving optimal transformations of the building envelope. Specifically, the model first employs an Artificial Intelligence Algorithm to generate the Bayesian Network structure required for overall climate adaptability scoring. Secondly, utilizing the Chinese Standard Weather Data (CSWD), the GRASSHOPPER algorithm is applied to implement variable parametric design on the facade skin, generating dynamic parametric skins and visual climatic data analysis cloud maps for energy benefit assessment. Finally, facade updates are executed based on the overall climate adaptability scores. The results demonstrate that the proposed framework effectively enables the real-time selection of optimal morphologies and opening states for dynamic skins based on comprehensive climatic adaptability criteria. Following model training and validation using 2025 Panjin meteorological data in the EnergyPlus Weather (EPW) format, the generated facade morphologies yielded solar radiation gains of 166.9 kWh/m²·month (peak month) for one of the optimal summer configurations and 90.5 kWh/m²·month (December) for one of the optimal winter configurations. Furthermore, by providing definitive evaluations of PV energy yields and indoor comfort levels across diverse weather scenarios, this framework offers explicit guidance for skin design, thereby reconciling the multi-objective optimization relationship between building energy conservation and occupant comfort. Full article

The consideration of consecutive seismic events remains a notable deficiency within current seismic design practices for engineering structures. The progressive accumulation of damage from an earthquake sequence poses a significant risk to the structural integrity of buildings, even those designed to exhibit ductile seismic behavior. The Kahramanmaras earthquake sequence in 2023 presented a unique opportunity to evaluate the contribution of multiple earthquakes on seismic damage in reinforced concrete (RC) buildings. The primary objective of this research is to quantify the variation in structural damage within low- to mid-rise RC buildings as a result of consecutive seismic events occurring within a short time period. Utilizing recorded ground motions and representative numerical models for RC buildings, the incremental damage sustained by RC building typologies subjected to the Kahramanmaras earthquake sequence is analyzed. This study examines the incremental increase in the probability of reaching predefined damage levels, comparing the effects of the first event alone with those of the complete earthquake sequence. The results demonstrate a significant contribution of subsequent events to structural damage accumulation, increasing overall collapse rates by approximately 2% on average, with the most severe impact observed in low code-compliant buildings where up to 9.2% of moderately damaged structures progress to collapse. Furthermore, conditional damage transition probabilities are derived, establishing a Bayesian updating framework to recalibrate post-sequence empirical fragility curves for single-event assessments. Full article

Rapid cooperative detection of stationary targets by multiple unmanned aerial vehicles (UAVs) is important in time-critical missions such as search and rescue. However, the online coordination of probabilistic inference, distributed communication, and detection–motion decisions under local information remain challenging when targets exhibit spatial correlations that existing methods typically neglect. To address this challenge, we develop a distributed event-driven Bayesian search framework for stationary, spatially correlated targets at unknown locations. The framework couples three components. A pairwise spatial model and a distance-dependent Neyman–Pearson detector yield a Bayesian belief update whose unclipped product form is order-invariant to event-processing sequence. A distributed selective flooding algorithm propagates only positive detection events, achieving finite-time event-set consensus over connected graphs while avoiding full-map exchange. A decoupled detection–motion planner exhausts high-belief cells within each UAV’s field of view before selecting a waypoint that balances surrogate detection probability against travel cost, with responsibility regions dynamically renegotiated among neighbors when local high-value cells are depleted. In numerical experiments, the proposed method achieved zero uncoordinated repeat detection in all simulations and significantly reduced first-discovery coverage relative to static-partition and no-communication baselines, while adapted external baselines required 90-fold and 6-fold larger communication payloads and had nonzero repeat-detection rates. The framework thus occupies a specific tradeoff point of zero revisit, sparse communication, and early discovery gain in scenes where targets span multiple UAV search regions. Full article

Channel fading and impulse noise in low-voltage station areas severely hinder data acquisition, and effective estimation of dual-mode channel data and state is the critical prerequisite for adaptive transmission. To address the impact of impulse noise on the performance of Orthogonal Frequency Division Multiplexing (OFDM) power line communication systems, this paper proposes a wireless-assisted joint estimation algorithm of channel fading and impulse noise in dual-mode OFDM systems. Firstly, a model of the dual-mode OFDM system combining power line and wireless links is established, and the initial channel estimation is obtained based on Linear Minimum Mean Square Error (LMMSE), the wireless link is used as an aid to provide a relatively reliable initial symbol estimation for the power line link. Then, a two-stage approach for initial impulse noise estimation is proposed: firstly, the possible impulse noise is located based on constructed residuals to form a support set, followed by refinement of the impulse amplitude using Single Measurement Vector Sparse Bayesian Learning (SMV-SBL). Finally, a decision-feedback iterative update is performed to iteratively optimize the symbol detection, channel estimation, and impulse noise estimation processes. Combined with adaptive dual-mode fusion, this approach improves estimation accuracy. Simulation results demonstrate that the proposed algorithm achieves significant improvements in estimation accuracy and bit error rate. Full article

To address the challenge of fatigue life prediction caused by multiple-crack interactions in ship and offshore structures, this study proposes a dynamic Bayesian network (DBN)-based method for predicting the fatigue life of structures with multiple cracks, which is systematically validated through physical experiments. First, a numerical model of a representative structure containing a central hole and multiple initial cracks was established based on the coupled simulation platform of ABAQUS and Franc3D. The nonlinear interaction behavior among multiple cracks under different geometric configurations was systematically investigated. Subsequently, a neural network surrogate model was developed, in which geometric features and crack lengths were employed as inputs and key fracture mechanics parameters were taken as outputs, enabling efficient prediction of complex stress intensity factor (SIF) fields. On this basis, fatigue crack growth experiments were conducted on DH36 high-strength steel specimens containing multiple cracks, and crack evolution data under realistic cyclic loading conditions were obtained. Finally, by coupling the surrogate model with the Paris law as the state transition equation and incorporating sparse experimental observations as dynamic updating information, a dynamic Bayesian network framework based on the particle filtering algorithm was established. This framework enables posterior probability tracking of multiple-crack fatigue states and rolling prediction of the remaining fatigue life. The results demonstrate that the proposed method can effectively mitigate the error accumulation associated with deterministic simulation models during long-term open-loop prediction while relying only on a limited number of discrete observation anchors. Consequently, the prediction accuracy of the fatigue life of multiple-crack systems is significantly improved. Furthermore, under crack co-propagation conditions, the proposed framework exhibits a strong capability to capture the propagation retardation of secondary cracks induced by shielding effects. The proposed method provides a theoretical foundation and technical support for the dynamic assessment of fatigue damage and the development of digital twins for complex structures containing multiple cracks. Full article

Reliable navigation in GPS-denied environments remains a critical challenge for autonomous vehicles (AVs), particularly in complex indoor and urban settings. GPS-based localization systems often fail under these conditions, highlighting the need for resilient multimodal solutions. In this article, we present a radar-assisted tracking system that integrates LiDAR and inertial measurements within a sensor-fusion architecture to achieve robust navigation. The principal methodological contribution is a unified tracking and prediction framework that combines Bayesian state estimation with learning-based temporal prediction, enabling accurate tracking while continuously forecasting the slave robot’s short-term future state from mapping observations generated by the master robot, with a typical end-to-end perception-to-action latency of 20–60 ms. The communication and prediction forecasting module operates with an update interval below 35 ms, enabling real-time cooperative robotic operation. Sensor data are fused through a pipeline incorporating Gaussian Mixture Models (GMMs) for post-processing, which helps mitigate the limitations associated with individual sensors during edge processing. Moreover, Kalman filtering is employed to mitigate sensor noise and drift, thereby improving state estimation accuracy through trajectory smoothing. The fused spatiotemporal information is subsequently exploited by a Convolutional Recurrent Neural Network (CRNN) coupled with a Nonlinear Autoregressive model with eXogenous Inputs (NARX) to model the robot’s motion dynamics and provide short-horizon state prediction. Through simulations and real-world indoor experiments conducted in GPS-denied environments, we validate the system’s ability to provide accurate and continuous pose estimation with low localization errors. Experimental results show that the proposed framework achieves root-mean-square errors of 0.12 m, 0.15 m, and 0.28 m along the X, Y, and Z axes, respectively, while maintaining sub-meter maximum position deviations throughout the evaluated trajectories. These results confirm that the proposed framework provides reliable localization and predictive state estimation for cooperative robotic navigation in indoor GPS-denied environments. Future work will investigate outdoor validation and extend the framework to additional data-driven decision-making models for future robotic services. Full article

This paper presents a predicted information and predicted action (PIPA) decision-analysis approach with Bayesian material updating for planning material testing in existing reinforced concrete members. The objective is to select the number of concrete and reinforcing-steel tests before testing is performed and before a management action is chosen, accounting jointly for the updated structural performance, information-acquisition costs, action costs, and expected failure consequences. Predicted future test outcomes are used to update the material-strength distributions; the updated distributions are then propagated through the shear, flexural, and system reliability analyses to inform outcome-dependent action selection. Management interventions are modelled as system-state actions through action-dependent system failure probabilities. The optimal testing option is identified by minimising a total predicted-information and predicted-action cost-and-risk measure that combines information and expected action costs with the expected consequences of the system states. The approach is applied to a benchmark reinforced-concrete member with transverse shear reinforcement and uncertain concrete compressive strength and reinforcing-steel yield strength, in which the same steel-strength population is adopted for the longitudinal and transverse reinforcement. The decision-optimal testing option consists of six concrete tests and three reinforcing-steel tests, reducing the total expected decision cost-and-risk measure by approximately 58.2% relative to the no-new-information decision. The decision value arises mainly from avoiding unnecessary intervention when favourable material information is acquired. The results formulate material-test planning as a decision-value problem, providing an alternative to fixed sample-size rules while retaining explicit dependence on structural, probabilistic, action, and cost assumptions. Full article

Directed energy deposition (DED) of low-alloy steels involves strongly coupled effects among alloy composition, solidification behavior, and post-deposition heat treatment, making mechanical property prediction difficult when target-domain data are limited. This study develops a transfer-learning and continuous optimization framework for predicting heat treatment-dependent yield strength (YS), ultimate tensile strength (UTS), hardness (HV), and as-solidified phase fractions of martensite, ferrite, and austenite in DED-processed low-alloy steels. A CALPHAD-based dataset was generated for 125 low-alloy steel compositions. A multilayer perceptron (MLP) surrogate was first trained as a baseline model, then fine-tuned through transfer learning and progressively updated as staged continuous optimization; the composition pool increased from 72 to 125 compositions using Random, Greedy, and Bayesian upper-confidence-bound acquisition strategies. The heat treatment prediction accuracy improved from an average R² of 0.757 for the baseline model to 0.929 after transfer learning and to approximately 0.997 after continuous optimization, with a nearly 78% reduction in RMSE relative to transfer learning. For the solidification outputs, the average R² increased from 0.770 after transfer learning to approximately 0.859 after optimization. Bayesian-UCB provided the most stable and data-efficient improvement by balancing predicted performance with model uncertainty. The optimized prediction system showed low case-study errors for both solidification and heat treatment properties, demonstrating its potential as a rapid screening tool for alloy composition and tempering-condition selection in DED low-alloy steel development. Full article

Approximate transport maps can facilitate exploration of a Bayesian target distribution, but the resulting samples generally do not follow that distribution. To address this problem, we develop residual-slice correction, a sampling framework that combines slice sampling with an approximate transport map held fixed during sampling. Each iteration uses a slice variable to represent the residual left by the map and updates the state while preserving the conditional distribution on the resulting feasible set. To assess sampling efficiency, we derive a lower bound on the corrected chain’s Dirichlet-form gap using a reference Markov kernel. The bound separates movement within each feasible set, the transport–reference comparison, and reference mixing, while projected diagnostics examine the first two factors. Numerical experiments show that residual-slice correction recovers summaries and shape diagnostics distorted by approximate transport; they also show that the choice of Markov update within each feasible set substantially affects mixing efficiency, and that the corrected chains have lower serial dependence after normalizing-flow training. Overall, the framework retains the geometric benefits of approximate transport while preserving the target distribution. Full article

An Adaptive-Parameter Maximum Correntropy Kalman Filter (APMCKF) algorithm is proposed to address state estimation degradation in semi-active suspensions caused by non-linear coupling between time-varying physical parameters and non-Gaussian noise. First, a time-varying dynamic model with non-linear damping is established via bench tests. A genetic algorithm (GA) globally optimizes key physical parameters to suppress model mismatch. Second, the APMCKF integrates an adaptive suspension parameter update mechanism. This closed-loop mechanism refreshes the system state matrix in real-time, effectively overcoming state-tracking lag. Concurrently, the maximum correntropy criterion (MCC) is embedded within the Sage–Husa recursive framework to dynamically reconstruct the observation noise covariance matrix, ensuring robust filtering under heavy-tailed noise. Simulations under ISO Class A–D random road profiles demonstrate that the APMCKF reduces the root-mean-square error (RMSE) by 62.33–81.24% compared to the adaptive Kalman filter (AKF). It also outperforms the adaptive-parameter Kalman filter (APKF), yielding a 27.49% accuracy improvement on Class D roads where non-Gaussian noise is most severe. Moreover, comparative evaluations against standard non-linear Bayesian filters demonstrate that the APMCKF successfully overcomes the truncation errors of the Extended Kalman Filter (EKF) and the tracking hysteresis of the Unscented Kalman Filter (UKF), reducing the average RMSE by up to 74.98% and 60.76%, respectively, under severe Class D non-Gaussian excitations. Furthermore, the algorithm exhibits excellent disturbance rejection under transient speed bump impacts and maintains stable error reduction across vehicle speeds of 10–25 m/s. Ultimately, the APMCKF delivers high-precision estimation and exceptional robust stability under variable speeds and non-Gaussian disturbances. Full article

Reliable mechanical parameters are needed to predict deformation during staged slope excavation, yet deep inclinometer profiles are high-dimensional and repeated numerical inversion is costly. This study integrates principal component analysis, Gaussian process regression (GPR), and sequential Bayesian updating to infer six effective parameters from multi-stage horizontal displacement profiles of a highway slope in Shaoyang, China. FLAC3D simulations were performed for a 120-point Latin hypercube design. Three principal components explained over 99% of profile variance. Ten repetitions of five-fold cross-validation yielded mean PCA–GPR $R^2$ values of 0.9769–0.9945. Empirical coverages of the 95% marginal prediction intervals ranged from 93.2% to 96.0%, and the GPR predictive covariance was included in the likelihood. Truncated multivariate Gaussian approximations were used to transfer posterior means and covariance structures between excavation stages. A five-strategy comparison showed that adding Stage 3 reduced parameter standard deviations by 10.0–22.0%, followed by a further 11.1–32.2% reduction after Stage 4. Sensitivity and posterior-contraction analyses indicated stronger constraints on the stiffness parameters, whereas ${\varphi }_2$ remained weakly identifiable. The final posterior means reproduced the spatially held-out, within-site CX-2 profile with a mean absolute error of 0.18 mm. The framework quantifies the parameter-specific information gained from complete profiles across excavation stages; its findings remain conditional on the monitored site and the adopted modeling and error assumptions. Full article

In complex operational environments, signal ambiguity and false alarms make task allocation for multiple unmanned aerial vehicles (UAVs) challenging. To address this problem, we propose the Bayesian Rolling Information-Theoretic Reconnaissance Action Assignment (RITRA) framework. RITRA integrates Bayesian belief tracking, expected information gain (EIG), and a cooperative criticality model based on Shapley values in a unified receding-horizon strategy. By assessing reconnaissance and intervention utilities for each UAV–hazard pair, RITRA casts mission planning as a dynamic optimization problem. We use an enhanced Hungarian algorithm with idle states, allowing UAVs to remain on standby when no deployment has positive expected value. Ablation results indicate that combining Bayesian tracking, active information collection, and risk-aware execution contributes to robust performance under the evaluated uncertainty conditions. In the 200-run paired Monte Carlo stress test, RITRA achieved a mean mission utility of 9.4362, a normalized mission score of 0.7890, and 0.245 false deployments per run. Against the best-performing baseline, a CBBA-based allocation method, RITRA achieved a mean paired mission-utility gain of 1.4925. On publicly released dynamic MRTA instances augmented with false-alarm uncertainty, RITRA achieved the highest aggregate mission utility, normalized mission score, and mission accuracy among the compared controllers, providing additional evidence of its effectiveness. These results indicate that RITRA supports risk-aware multi-UAV allocation under the evaluated uncertain conditions. Full article

Background: The EU Health Technology Assessment Regulation (EU-HTA R), effective January 2025, mandates Joint Clinical Assessments (JCAs) to harmonize HTA across Member States. However, its implementation raises fundamental questions about methodological coherence, institutional capacity, and epistemological alignment. Objectives: This manuscript (1) systematically assesses whether the stated strategic and operational objectives of the EU-HTA R are achievable under current implementation conditions; (2) examines the implications for EU institutional legitimacy if these objectives are not met; and (3) proposes an epistemological framework as a prerequisite for developing a coherent joint HTA methodology. Methods: We conducted a critical policy analysis of the EU-HTA R, its implementing guidance documents, and published templates, supplemented by a comparative review of Member State HTA methodologies and their underlying philosophical foundations. Results: The analysis reveals that the EU-HTA R is unlikely to achieve its strategic goals under current conditions. Key findings include: guidance documents of substandard methodological quality; a restricted assessment scope that excludes scientific judgement and contextualization; insufficient resources and additional workload for national HTA bodies without reducing existing obligations; unresolved epistemological divergences among Member States spanning Bayesian vs. frequentist approaches, Fisher vs. Neyman–Pearson frameworks, and utilitarian vs. deontological ethical foundations; and procedural shortcomings in stakeholder consultation and expert involvement. These shortcomings risk undermining the epistemic authority and legitimacy of EU institutions. Conclusions: Prior epistemological and normative alignment across Member States is a prerequisite for any robust shared HTA methodology. Revisions to the EU-HTA R and comprehensive updates of guidance documents are necessary, with concrete safeguards—including independent peer review, identified authorship, and adequate resourcing—to ensure substantive rather than merely nominal implementation. A phased roadmap is proposed: establishing clear objectives, aligning epistemological foundations, developing institutional structures, and creating operationally consistent guidance. Full article