Search Results (1,416)

To accurately assess the time-dependent reliability of bridge piers in marine environments, this paper proposes a time-dependent reliability evaluation method for bridge piers based on a Dynamic Bayesian Network (DBN). By establishing a resistance degradation model under the combined effects of reinforcement corrosion and concrete deterioration and embedding it into the DBN framework, dynamic prediction and updating of pier time-dependent reliability are achieved. A case study of a twin-column pier was conducted for verification, showing that the computational results of the proposed DBN model align well with the first-order reliability method (FORM), validating its accuracy and feasibility in time-dependent reliability prediction. Further, using the most severe service condition, that is, the tidal-spray zone as an example, the DBN prediction results were updated with inspection data to achieve dynamic assessment of the actual pier lifespan. Additionally, a comparative analysis of environmental zones revealed that the tidal-spray zone exhibits the fastest reliability degradation, followed by the atmospheric zone, while the submerged zone shows the slowest. Full article

Background. Human populations are exposed to complex chemical mixtures, making interaction detection a central challenge in environmental epidemiology. We benchmarked methods for prediction and recovery of interaction structure. Methods. Eight approaches—main-effects Lasso (glmnet_main), interaction Lasso (glmnet_int), hierNet, Random Forests, Bayesian Kernel Machine Regression (BKMR), quantile g-computation (qgcomp), weighted quantile sum regression (gWQS) and SuperLearner—were evaluated across eight linear/nonlinear, additive/interaction, continuous/binary data-generating processes (500 replicates each). Every method completed in all 500 replicates of all eight scenarios. Prediction was assessed on held-out test data using observed-outcome and oracle-referenced metrics; interaction detection was assessed against three known pairwise interactions among 45 candidate pairs, using both hard selection and a threshold-free ranking criterion. BKMR was evaluated at 2000 versus 25,000 MCMC iterations with multi-chain convergence diagnostics. Sensitivity analyses varied sample size, exposure correlation, signal strength, and interaction form. BKMR was also applied illustratively to six metals and prevalent diabetes in NHANES. Results. In additive settings, observed-outcome prediction was similar across methods, but oracle-referenced continuous-outcome error differed by up to six-fold. With interactions, interaction-aware methods clearly outperformed additive-only approaches on the continuous oracle-referenced metrics: in LMI, the oracle MSE was 1.57 for hierNet and 1.87 for glmnet_int against 3.80 for glmnet_main and 4.94 for qgcomp. glmnet_int and hierNet showed comparable sensitivity; hierNet had a modestly lower mean per-replicate false discovery proportion in paired comparisons, while pooled false discovery favored hierNet in the continuous scenarios and glmnet_int in the binary ones; pooled false discovery rates were 0.79 to 0.82 in every interaction scenario, so roughly four in five selected pairs were false. In the scenarios without true interactions, the pooled false discovery rate was exactly 1. Under threshold-free ranking, BKMR was competitive with the penalized methods (pair-ranking AUC: 0.758 to 0.781 across the four interaction scenarios). BKMR’s apparent instability at 2000 iterations reflected inadequate sampling: 93% of monitored parameters had a Gelman–Rubin statistic above 1.1 and the minimum effective sample size was 7.5, whereas at 25,000 iterations the median statistic was 1.02 and the oracle MSE in LMI fell from 6.38 to 2.08. In NHANES, lead, manganese, and iron had the highest posterior inclusion probabilities, with predominantly nonlinear exposure–response functions. Conclusions. Method choice matters most when interactions are present. Interaction-aware methods are preferable when joint effects are relevant, selected interactions require replication given the high false discovery burden, and BKMR comparisons should report sampling budgets and convergence diagnostics rather than treating a short chain as characteristic of the method. Full article

Non-inferiority (NI) clinical trials have gained immense popularity within the last decades, especially in cancer and cardiovascular studies. These trials are designed to establish the non-inferiority of a new experimental treatment as compared to the existing active control. In other words, NI trials are required to demonstrate that the efficacy of an experimental treatment is not unacceptably worse than that of an active control by more than a pre-specified small margin. We consider three-arm NI trials that have been widely acknowledged as the Gold Standard. Three-arm NI trials aim to simultaneously establish both NI and the assay sensitivity (AS). Hence, the analysis of three-arm NI trials involves multiple hypothesis testing. The existing literature on the Bayesian modeling of three-arm NI trials suggests implementing a test procedure based on the joint posterior probability of the NI and AS hypotheses. This joint testing of NI with AS resembles the framework of intersection-union (IU) testing, which may result in a very conservative test. In this article we propose a novel Bayesian testing based on an isotonic transformation in conjunction with Bayes factors. Bayes factors for assessing NI with AS are computed based on Gibbs Sampling. The performance of the proposed testing is evaluated through simulated data sets under varying scenarios. Empirical results show that the proposed Bayesian method gives better control in terms of Type-I error rates, and more powers than existing Bayesian tests. The usefulness of our test is illustrated by synthetic data from the Mildly Asthmatic Study. Full article

Background: Multiple myeloma (MM) remains an incurable plasma cell malignancy characterized by marked clinical heterogeneity. Existing prognostic frameworks, including the International Staging System (ISS) and FISH-defined cytogenetic risk, are anchored at diagnosis and do not capture the evolutionary dynamics of disease or treatment response, leaving an unmet need for risk models that retain prognostic validity longitudinally. Methods: Using transcriptomic data from 762 CD138-selected MM plasma cells from newly diagnosed patient samples in the MMRF CoMMpass study (NCT01454297), we computed single-sample pathway activity scores for 469 curated cancer-relevant pathways (MSigDB Hallmark; Reactome) and learned a Bayesian causal network linking pathway activity to survival. The model was validated in five independent diagnostic cohorts (n = 1255) and in two independent treatment and relapsed/refractory cohorts (n = 319). Longitudinal risk tracking was additionally assessed in a 46-patient subset of the discovery cohort with serial pre- and post-treatment sampling. Results: The network identified five pathways associated with survival: unfolded protein response (UPR), FLT3 signaling through SRC family kinases, G2M DNA replication checkpoint, metabolism of selenium compound (SeMet), and nicotinate metabolism. The composite survival score stratified patients into high-risk (n = 76; 10%) and standard-risk groups with markedly divergent survival (median 1170 days vs. not reached; p < 0.0001). The score remained an independent prognostic factor after adjustment for age, sex, ISS stage, and KRAS, TP53, and UBR5 mutational status (HR 4.93; 95% CI 2.96–8.19; p < 0.001), and replicated across all five external diagnostic cohorts. Critically, the model retained prognostic discrimination in previously treated (GSE57317; p < 0.0001) and relapsed/refractory (GSE9782; p < 0.0001) settings, and patients transitioning from standard- to high-risk between serial samples exhibited significantly inferior survival compared to standard-risk patients. Conclusions: This pathway-based Bayesian network provides a reproducible, dynamically applicable risk model for MM that captures information complementary to ISS and FISH-defined cytogenetics. The framework supports longitudinal patient monitoring and may inform trial enrichment strategies and closer surveillance for high-risk subpopulations. Full article

Solar thermal energy storage systems play a vital role in bridging the gap between intermittent solar availability and continuous energy demand; however, their efficiency is severely constrained by the inherently low thermal conductivity of phase change materials. Integrating physical heat transfer enhancements, such as radial fins, offers a practical solution, but evaluating these non-linear thermal dynamics across diverse design configurations typically incurs heavy computational costs. To address this challenge, this research investigates an artificial intelligence-based predictive framework capable of accurately modeling complex phase change dynamics in fin-configured storage tanks. Utilizing high-fidelity 2D Computational Fluid Dynamics simulation data of a stainless-steel double-tube storage tank filled with RT-50 paraffin wax across 10, 20, and 29 fin configurations, a Multi-Layer Perceptron Artificial Neural Network trained with the Bayesian Regularization algorithm was developed. The model predicts liquid fraction, latent heat distribution, and buoyancy-driven natural convection (Reynolds number) based on fin count and time. The optimal architecture, featuring 30 hidden neurons, achieved exceptional predictive precision, yielding a coefficient of determination of 0.99999, along with individual Mean Squared Error values of 1.29 × 10⁻³ for liquid fraction, 5.73 × 10⁻¹ for latent heat distribution, and 2.64 × 10⁻⁵ for Reynolds number, with average prediction deviation rates consistently below 0.5%. These results demonstrate that high-precision surrogate modeling can effectively replace computationally intensive numerical simulations, offering significant practical implications for the real-time thermal monitoring, rapid design optimization, and intelligent control of advanced solar energy storage technologies. Full article

Distributed Scatterer Interferometric Synthetic Aperture Radar (DSInSAR) technology has been widely applied in areas with complex terrain and dense vegetation. However, DSInSAR is computationally intensive and requires considerable processing time. When new observations become available, the entire dataset must be reprocessed without utilizing previously obtained results. This makes DSInSAR unsuitable for long-term continuous monitoring. The Sequential Estimator partitions large datasets into fixed-size subsets and compresses these subsets to avoid redundant processing. The Recursive Sequential Estimator with Flexible Batches (RSEFB) method was proposed to partition large datasets into flexibly sized subsets. However, how to determine appropriate grouping boundaries remains unresolved. In this paper, a Change-Point-Based Deformation Grouping Strategy (CPDGS) is proposed to enhance the deformation estimation accuracy within each group, thereby reducing the attenuation of abrupt deformation signals during estimation. In the proposed method, a Bidirectional Long Short-Term Memory (Bidirectional LSTM) network is employed to identify the potential presence of deformation change points. Bayesian Estimator of Abrupt change, Seasonality and Trend (BEAST) is subsequently used to localize the change points. Considering computational efficiency, an upper limit is also set on the number of Single Look Complex (SLC) per group. Due to the lack of ground truth, simulated data were used for the network training. Comparative experiments show that the proposed Bidirectional LSTM achieves the best overall performance, with an accuracy of 88.43%, a precision of 90.76%, a recall of 86.04%, and an F1-score of 88.34%, outperforming the LSTM and Transformer models. Further comparisons with conventional change point detection methods show that the proposed method achieves an F1-score of 91.23%, higher than Cumulative Sum (CUSUM; 69.20%) and and Bayesian Online Change Point Detection (BOCPD; 84.44%). Experiments using real Interferometric Synthetic Aperture Radar (InSAR) deformation data further demonstrate its effectiveness in identifying deformation change points in practical scenarios. Full article

Flood forecasting often depends on upstream hydrographs that are unavailable, incomplete, or unreliable. Reverse Flood Routing (RFR) addresses this gap by reconstructing upstream inflows from downstream observations, yet its operational use remains limited by numerical instability, observational uncertainty, and the ill-posed nature of the inverse problem. This review critically synthesizes RFR methodologies across a physics–fidelity continuum, ranging from storage-based and simplified hydraulic models to full hydrodynamic inversions, optimization-based techniques, Bayesian approaches, and emerging data-driven methods. The reviewed approaches are compared in terms of physical realism, numerical stability, computational demand, data requirements, uncertainty treatment, and field applicability. The synthesis indicates that storage-based methods remain attractive for data-limited and computationally constrained applications, whereas full hydrodynamic models are better suited to complex flow conditions involving backwater effects and detailed channel hydraulics. Optimization-based and Bayesian approaches can improve parameter estimation and uncertainty representation, while hybrid AI–physics methods offer promise for computational acceleration but still require stronger physical constraints and broader operational validation. Across all methodological families, error amplification, lateral inflow, transmission losses, and inconsistent benchmarking remain persistent limitations. An integrated framework is therefore proposed to connect observations, model selection, regularization, uncertainty quantification, hybrid computational methods, and operational decision support, providing a roadmap for more reliable and scalable RFR applications. Full article

Distributed multiple-input multiple-output (MIMO) is a promising architecture for future wireless systems because cooperation among geographically separated base stations (BSs) improves coverage, spectral efficiency, and link reliability. However, the large effective aperture formed by distributed BSs makes near-field effects non-negligible and complicates accurate channel state information acquisition. Existing near-field estimators often suffer from modeling errors caused by approximate angle–range decoupling or from the high storage and computational costs of dense two-dimensional sparse representations. This article proposes an off-grid variational Bayesian channel-estimation framework for the considered distributed near-field MIMO geometry, which comprises equally spaced, collinear BS reference points and aligned uniform linear arrays (ULAs) with common inter-element spacing. We establish a geometry-coupled model based on the exact geometric spherical-wave phase response and map the local direction–range parameters observed by different BSs into a common reference coordinate system, yielding a two-dimensional jointly sparse representation. An independent-vector variational Bayesian inference algorithm then decomposes the high-dimensional multiuser recovery problem into user-specific posterior subproblems. It operates directly on the received pilot matrices, avoiding pilot–matrix inversion and the resulting distortion of noise statistics. A two-dimensional skewed off-grid update is further embedded in an expectation-maximization procedure to jointly refine angle and range offsets, mitigating basis mismatch while permitting a coarser initial dictionary. Simulation results support the effectiveness of the proposed method in the evaluated scenarios. Full article

Traditional P300 brain–computer interface (BCI) systems rely on multi-channel EEG acquisition, causing cumbersome setup, lengthy preparation, and high user workloads, which limits their real-world application. To enhance practicality, this paper proposes a fixed few-channel selection framework based on a heuristic algorithm to balance decoding performance and user experience. We integrated a genetic algorithm (GA) with Bayesian linear discriminant analysis (BLDA) to identify a strongly generalizable few-channel combination from a traditional eight-channel system, avoiding costly subject-specific recalibration. Validating this method, 48 healthy subjects completed rigorous offline and online virtual reality (VR) experiments. Results showed that the proposed three-channel system maintained highly comparable accuracy and information transfer rates to the eight-channel system, showing no significant performance degradation. Crucially, the few-channel scheme reduced equipment preparation time by 90% (from 30 to 3 min). Furthermore, NASA-TLX workload evaluations confirmed a significant reduction in users’ psychological and physical burdens (p < 0.05). Ultimately, while preserving core interaction performance, this few-channel strategy vastly improves user experience and system practicality, offering key theoretical and practical support for implementing lightweight, user-friendly BCI systems. Full article

Recommender systems increasingly incorporate graph embeddings and graph neural networks to capture high-order relationships between users and items. However, the additional complexity of these approaches does not necessarily guarantee better recommendation quality than strong classical and latent-factor baselines. This study presents a reproducible comparison of six recommendation models representing four methodological families: Logistic Regression and Random Forest; Matrix Factorization with Bayesian Personalized Ranking; DeepWalk and node2vec; and LightGCN. The experiments were conducted on the MovieLens 1M dataset using a per-user temporal split. For each user, the most recent positive interaction was assigned to testing, the preceding interaction to validation, and all earlier positive interactions to training. The primary evaluation used identical candidate sets containing one held-out positive movie and 99 sampled unobserved movies. Performance was measured using Recall, Precision, Hit Rate, and NDCG at multiple cutoffs, complemented by bootstrap confidence intervals, paired statistical tests, computational-efficiency measurements, and analyses by user activity and movie popularity. Matrix Factorization achieved the best overall performance, reaching a Recall@10 of 0.7458 and an NDCG@10 of 0.4558, representing an approximately 56% improvement in NDCG@10 over Random Forest, the strongest classical baseline. Validation-based tuning improved LightGCN to an NDCG@10 of 0.2875; it significantly outperformed Logistic Regression but remained statistically indistinguishable from Random Forest after Holm correction. Tuned node2vec also significantly outperformed DeepWalk, reaching an NDCG@10 of 0.1593, although both random-walk embedding methods’ results remained substantially below than the strongest baselines. Popularity-based analysis further revealed that classical models and LightGCN achieved substantially higher ranking effectiveness for popular movies, whereas Matrix Factorization maintained comparatively stronger performance for less-popular items. These findings show that under the evaluated setting, greater model complexity did not consistently translate into higher recommendation effectiveness, and they thus highlight the importance of strong baselines, model tuning, standardized evaluation, and reproducible experimental protocols. Full article

Earthquakes remain among the most destructive natural hazards, necessitating reliable monitoring and early warning systems for effective risk mitigation. Recent advances in machine learning (ML) and deep learning (DL) have significantly improved seismic signal analysis, enabling more accurate event detection, phase picking, classification, and magnitude estimation. This study presents a systematic review of ML- and DL-based approaches for earthquake monitoring, with particular emphasis on Distributed Acoustic Sensing (DAS) as an emerging technology for high-resolution, real-time seismic observation. Following the PRISMA 2020 guidelines, a systematic literature search was conducted across Scopus, Web of Science, IEEE Xplore, and Google Scholar, yielding 252,223 initial records. After applying the predefined publication period, removing duplicate records, conducting relevance screening, and performing eligibility assessment, 138 peer-reviewed studies published between 2021 and 2025 were retained for detailed analysis and synthesis. The review reveals a significant transition from conventional signal-processing techniques to advanced artificial intelligence-based approaches, including Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, Bidirectional Long Short-Term Memory (BiLSTM) networks, Transformer-based architectures, hybrid models, and Bayesian learning methods for uncertainty quantification. The findings further demonstrate that Distributed Acoustic Sensing (DAS) has emerged as a transformative sensing technology because of its dense spatial coverage, high spatial resolution, and continuous monitoring capability. However, several challenges remain, including the lack of standardized datasets, limited model generalization across diverse geological settings, insufficient model interpretability, high computational complexity, and the limited integration of uncertainty-aware approaches for real-time seismic monitoring. This review identifies these critical research gaps and highlights promising future research directions, including multimodal data fusion, interpretable artificial intelligence, physics-informed learning, self-supervised learning, and robust uncertainty quantification for next-generation intelligent seismic monitoring systems. Unlike previous review studies that primarily focus on individual machine learning techniques or conventional seismic monitoring, this review provides a comprehensive and systematic synthesis of recent advances in machine learning, deep learning, and Distributed Acoustic Sensing (DAS), identifies current research gaps, and offers practical recommendations to guide future research on intelligent earthquake monitoring systems. Full article

Bounded observations occur in a variety of applied scenarios such as reliability probabilities, degradation measures, rates, and other functions defined on the unit interval for which flexible distributional and hazard-rate behavior are desired. This paper presents the Unit Harris Extended Exponential Distribution (UHEED), a three-parameter unit distribution derived from the Harris Extended Exponential Distribution to offer more flexibility in these types of data, including the ability to model bathtub-shaped hazard rates. Several mathematical properties are developed, such as parameter identifiability, quantile elasticity, moments, order statistics, and Shannon entropy. Maximum likelihood estimation is used as classical inference; the squared error and LINEX loss functions are used to develop the Bayesian estimation with a Metropolis–Hastings within Gibbs algorithm. A representative-point approximation is also proposed for evaluating important distributional quantities, including moments and reliability measures. The Monte Carlo results indicate that the more samples, the more accurate the results of the estimation. A useful example of the UHEED is presented with naturally bounded bramble cane spatial-coordinate data, and goodness-of-fit comparisons show that it performs competitively relative to the competing unit distributions used in the analysis. Full article

Sustainable polyurethane-based biocomposites (PUBs) reinforced with waste artichoke stem (WAS) and modified soybean oil (MSO) provide a promising approach for agricultural-waste valorization and the development of multifunctional polymeric materials. In this study, 48 PUB formulations are prepared by systematically varying WAS content from 0.0 to 3.5 wt.% and MSO content from 0 to 5 wt.%. The dielectric constant, thermal conductivity, Shore A hardness, bulk density, tensile strength, and elongation at break are experimentally evaluated. Across the investigated formulations, the dielectric constant, thermal conductivity, Shore A hardness, bulk density, tensile strength, and elongation at break range from 1.10 to 1.56, 0.024 to 0.036 W m⁻¹ K⁻¹, 9.5 to 43.0, 34.0 to 67.0 kg m⁻³, 115 to 297 kPa, and 62 to 176%, respectively. A multi-output artificial neural network (ANN) model is developed in MATLAB using WAS and MSO contents as input variables and the six experimentally determined properties as simultaneous outputs. The ANN architecture consists of two input neurons, one hidden layer with ten neurons, and six output neurons. Levenberg–Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugate Gradient (SCG) algorithms are comparatively evaluated using 42 samples for model development and six independent samples for external validation. The results demonstrate that the ANN successfully captures the nonlinear relationships between formulation variables and the investigated multifunctional properties. Among the evaluated training algorithms, BR provides the most accurate and robust predictive performance, followed by LM, whereas SCG exhibits comparatively lower prediction accuracy. The proposed experimental–computational framework enables reliable simultaneous prediction of the electrical, thermal, physical, and mechanical properties of PUBs and provides an efficient strategy for reducing experimental effort and accelerating the formulation and performance assessment of sustainable biocomposites. Full article

Renewable-generation forecasting is a probabilistic time-series problem in which point accuracy alone is insufficient for operational decision-making. This study proposes a physics-informed Bayesian framework for multi-horizon forecasting of solar, wind, and total renewable generation. The task is formulated as a 15-dimensional target–horizon problem covering three generation families and five forecast horizons: H1, H3, H6, H12, and H24. A leakage-safe data construction protocol generates causal predictors from meteorological observations, generation history, calendar cycles, lagged and rolling statistics, ramp descriptors, and physics-informed transformations. PI-BHTF partitions the 422-dimensional predictor space into solar, wind, temporal-calendar, and cross-context components, encodes them through parallel nonlinear branches, and combines the representations using cross-energy gated fusion. Its neural core uses a heteroscedastic predictive head and Monte Carlo dropout to distinguish input-dependent aleatoric uncertainty from epistemic variability, whereas the final hybrid forecasts are calibrated using residual quantiles computed from a chronologically held-out validation segment. Across three prespecified random seeds, the full PI-BHTF achieved a mean MAE of 0.0797 ± 0.0007 on the internal chronological test and 0.0686 ± 0.0006 on locked external SCADA validation. Performance and calibration varied substantially across target–horizon tasks, including marked short-horizon external undercoverage for wind generation. Component-wise ablation supported semantic feature partitioning and the heteroscedastic head, whereas the physics-informed features, cross-energy gate, and physics-consistency loss did not independently reduce aggregate MAE. PI-BHTF should therefore be interpreted as a reproducibly competitive framework that balances multi-horizon accuracy, structured representation, uncertainty estimation, and external transferability rather than as a universally dominant model. Full article

The widespread use of location-based services (LBSs) has led to the continuous collection of user location and trajectory data, increasing the risk of privacy leakage and creating a persistent tradeoff between privacy protection and data utility. To address this problem in discrete location query scenarios, this paper proposes a single-point location privacy protection method based on Q-R tree retrieval and differential privacy, termed QRDPP. QRDPP combines the adaptive spatial partitioning capability of a Q-tree with the minimum bounding rectangle (MBR)-based indexing capability of an R-tree. It applies an improved geometric privacy budget allocation strategy to leaf nodes and an arithmetic allocation strategy to non-leaf nodes, followed by Laplace perturbation of the corresponding location data and node information. For continuous trajectory query scenarios, this paper proposes a spatiotemporal generalization and differential privacy method, termed STG-DPTP, to address inadequate temporal protection, inappropriate generalization, and trajectory distortion. STG-DPTP performs hierarchical spatiotemporal clustering, separately models temporal and spatial distributions using Gaussian kernel density estimation, dynamically optimizes bandwidth parameters through Bayesian optimization, selects representative candidate subsets using the exponential mechanism, and generates protected trajectories through constrained sampling. Experiments on the GeoLife dataset evaluate the proposed methods in terms of query accuracy, computational efficiency, spatial trajectory similarity, reconstruction error, adversarial uncertainty, and temporal preservation. The results show that QRDPP improves the utility and efficiency of privacy-preserving spatial queries, while STG-DPTP better preserves the spatial distribution, trajectory structure, and temporal characteristics of the original data under the adopted differential privacy framework. Full article