Search Results (400)

Background: Osteoarthritis (OA) is a prevalent degenerative joint disease lacking effective disease-modifying therapies, which necessitates the discovery of key genes for mechanistic exploration and therapeutic development. Methods: We integrated three large-scale cis-protein quantitative trait locus datasets and two OA genome-wide association study summary statistics to screen candidate proteins by two-stage proteome-wide Mendelian randomization (MR). Causal association reliability was validated via summary-data-based Mendelian randomization (SMR) and Bayesian colocalization analyses. A phenome-wide association study (PheWAS) was performed to evaluate potential pleiotropic effects of the candidates. Subsequently, transcriptomic and single-cell RNA sequencing datasets were employed to evaluate the candidate genes’ expression stability, classification efficacy in the in vitro models of OA, cell-specific enrichment, and pseudotime expression dynamics in cartilage. Finally, drug repurposing potential was explored by integrating drug–gene interaction database searches and molecular docking. Results: Two-stage cis-pQTL MR combined with cis-eQTL-based SMR analysis identified 14 plasma proteins with consistent effects at the protein and transcript levels. RNA-seq revealed that adrenomedullin (ADM) and complement factor H (CFH) were upregulated in two in vitro models of OA, and both genes exhibited favorable classification efficacy in these models. Bayesian colocalization analysis provided evidence of shared causal variants for ADM, and PheWAS did not detect significant pleiotropic associations for ADM or CFH across the tested phenotypes. Single-cell analysis indicated that ADM was enriched in pre-fibrocartilage chondrocytes with biphasic pseudotime expression, whereas CFH was widely expressed across chondrocyte subsets. Database screening identified 15 potential drugs for ADM and 6 for CFH. Conclusions: Combining MR, multi-omics and pharmacological evidence, we prioritized ADM and CFH as OA candidate genes. Full article

We define a class of Markov cell processes, $P_X$, on a finite set, X, as a product of conditional probabilities on cells (subsets of X forming a partially directed intersection graph). The class of Markov cell processes includes Bayesian networks and Markov edge processes. A nested conditional independence (NCI) condition is introduced that allows an explicit expression of a joint probability distribution through its marginals. The NCI condition is used in our construction of consistent Markov cell processes $P_X$ whose marginals coincide with $P_{X^{\prime }}$ on smaller sets $X^{\prime }\subset X$. We discuss three classes of consistent Markov cell processes on rectangular domains $X\subset {\mathbb{Z}}^3$ equipped with ‘cubic’ cells, which include the Arak model and 3D Pickard model. Full article

Reconstructing a 3D human skeleton from partial joint observations is an ill-posed inverse problem: when joints are missing, infinitely many anatomically distinct poses fit the observation, but most methods return a single reconstruction. We formulate single-frame reconstruction as a linear inverse problem, characterize the null space of the missing joints, and argue that the output should be a distribution over the feasible set rather than a point estimate. We model this distribution as a Bayesian posterior with a diffusion model as a learned prior. The question is not whether such a prior can reconstruct skeletons, but when it is warranted: a conjecture relates the error of linear interpolation to the curvature of the pose manifold, with a low-curvature limit in which interpolation is near-optimal. Measured on NTU RGB+D, the curvature is non-zero and intrinsic to individual motions but moderate, and the results are as follows: the prior outperforms nearest-neighbor averaging under scattered occlusion up to moderate severity, is outperformed by it under structural occlusion, and yields per-joint uncertainty that tracks the realized error ($r\approx 0.7$). The prior satisfies the kinematic constraints implicitly: explicit guidance improves bone-length fidelity but degrades accuracy, so the constraints serve the formulation and the analysis rather than the sampler. Full article

This study proposes a Generalized Bayesian Structural Time Series (GBSTS) framework for forecasting seasonal time series with sparse observations. Rather than introducing new state-space components, the proposed framework systematically integrates alternative trend and seasonal specifications with multiple missing-data reconstruction strategies within a unified Bayesian state-space formulation. This integration enables the joint assessment of structural model specification and missing-data treatment under varying sample sizes, seasonal structures, and levels of data sparsity. Forecasting performance is evaluated through a Monte Carlo simulation study considering different seasonal mechanisms, missing-data rates of 10%, 30%, and 50%, and a fixed 12-month forecasting horizon. An empirical analysis of monthly temperature and relative humidity series is additionally conducted to assess the practical applicability of the proposed framework. The results show that the GBSTS with a local linear trend and trigonometric seasonal component achieved the best overall forecasting performance among the models considered, demonstrating robust predictive accuracy across varying data conditions and levels of sparsity. Among the imputation methods examined, Kalman smoothing and seasonal split generally yielded comparable forecasting performance across different data conditions and levels of sparsity. Overall, the findings indicate that integrating flexible structural specifications and missing-data reconstruction within the GBSTS framework provides an effective and interpretable Bayesian approach for forecasting seasonal time series with sparse observations. Full article

Energy demand forecasting is a critical task in sustainable energy systems, where nonlinearities, temporal dynamics, and multivariate factors pose significant challenges. Despite the success of deep learning methods, how to design and optimize deep learning algorithms is yet to be investigated for energy demand forecasting. To address these issues, we propose an input-adaptive dynamic convolution-augmented Transformer. Specifically, channel, phase, and joint channel–phase embeddings are designed to enrich feature representations. Then, a dynamic convolution module is developed to adaptively learn one-dimensional kernels via input-dependent adaptive attention. The resulting features are further fed into a Transformer encoder for forecasting energy demand. Finally, an efficient Bayesian optimization algorithm is used to automatically design and optimize the proposed method. Experimental results on real-world energy demand forecasting task demonstrate the effectiveness and practicality of the proposed method. Full article

The progressive development of 3D subsurface geotechnical maps requires integrating site-based voxel models produced from neighboring engineering-geological investigations, but directly joining separately interpolated classified voxel models can produce artificial boundary discontinuities. This study proposes a GIS-based workflow for reducing such discontinuities through Empirical Bayesian Kriging 3D (EBK 3D) re-interpolation, tested on two adjacent construction sites in Astana, Kazakhstan. Separate 1 × 1 × 1 m classified voxel models were generated for Site A and Site B and compared at their boundary using face-to-face and 1 m offset schemes. Before re-interpolation, the face-to-face boundary matching ratio (BMR) was 49.4%, and the boundary discontinuity ratio (BDR) was 50.6%. Two alternative re-interpolation strategies were then tested: joint-raw EBK 3D interpolation applied directly to the pooled borehole data, and a two-step approach joining and re-interpolating the previously interpolated per-site voxel tables. Both substantially improved face-to-face continuity (BMR rising to 99.1% for joint-raw and 95.7% for the two-step approach), but the two-step approach produced a considerably larger improvement in the surrounding offset neighborhood (BMR increasing to 74.8%, versus 56.9% for joint-raw), indicating it extends greater numerical continuity beyond the immediate contact layer. The two-step approach, used as the primary method, achieved a combined soil-type preservation ratio of 85.9% (88.3% for Site A, 82.7% for Site B). These results demonstrate improved numerical and visual interface continuity rather than independently validated geological accuracy; engineering application would require independent validation and uncertainty quantification. The approach supports the transition from isolated site-scale voxel models toward an expandable 3D subsurface geotechnical mapping system. Full article

Clock steering, the core time-frequency technology for high-precision single-satellite time reference generation in global navigation satellite systems, can effectively combine the excellent short-term stability of Oven-Controlled Crystal Oscillators (OCXOs)—whose top performance indicators have already surpassed many space-borne atomic clocks in recent years—with the superior long-term stability of atomic clocks, to obtain time signals with optimal full-range stability. This paper proposes a novel clock steering scheme that integrates an adaptive variational Bayesian Kalman filter and a Proportional-Integral-Derivative (PID) automatic controller: the filter constructs a separable variational approximation for the joint posterior distribution of clock states and measurement noise parameters, to achieve real-time adaptive estimation of noise at each timestamp, while the PID controller performs closed-loop fine adjustment on the output frequency. Comparative simulations with the classic Linear Quadratic Gaussian (LQG) control scheme verify that the proposed method can generate steered time signals with better stability performance in both short-term and long-term dimensions. This work further investigates the influence of measurement noise at different intensity levels on clock steering performance and conducts corresponding mechanism analysis supported by quantitative data. The proposed scheme and conclusions can provide a reliable reference for selecting appropriate clock steering strategies under different noise conditions. Full article

Reliable crop yield estimation is fundamental to food security and efficient agricultural management. However, current deep learning models still face limitations in selecting and integrating multi-source features, and their high predictive accuracy is often accompanied by limited interpretability. This study introduces a Bayesian Optimization–Temporal Convolutional Network–Bidirectional Long Short-Term Memory–Dual Attention (BO-TCBDA) deep learning framework for winter wheat yield estimation. Using Henan Province, China, as the study area, county-level winter wheat yield from 2013 to 2022 was estimated using the Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), Solar-Induced Chlorophyll Fluorescence (SIF), and climate data. The proposed model was compared with five commonly used machine learning and deep learning models. BO-TCBDA achieved the best performance, with an R² of 0.823 and an RMSE of 561.26 kg/ha. SIF improved the predictive performance of all models, with statistically significant gains observed in the deep learning models. The dual-attention mechanism provided interpretable insights by revealing relatively balanced contributions among the input features and highlighting the grain-filling stage through temporal attention. Furthermore, SHAP-based cross-validation analysis identified T12, corresponding to the latter part of the jointing stage, as the period with the highest contribution to yield prediction. The model also achieved an R² of approximately 0.80 about 25 days before harvest. Overall, BO-TCBDA provides an accurate and interpretable approach for county-level winter wheat yield estimation and supports regional food security assessments and precision agriculture. 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

In this article, we propose a new bivariate generalization of the inverted Topp–Leone distribution. First, we express its joint cumulative distribution and survival functions in series forms using special functions, which yields the bivariate hazard rate, reversed hazard rate, and the exact distributions of $\min \{X,Y\}$. We then establish several essential properties, such as marginal and conditional distributions, joint moments, entropy, and the Fisher information matrix. After proving that the distribution exhibits positive likelihood ratio dependence, we derive the exact distributions for the transformations $X+Y$, $X/(X+Y)$, and $XY$ when X and Y follow an inverted bivariate Topp–Leone distribution. Finally, we round out the statistical framework by presenting parameter estimation techniques, a simulation study, and Bayesian inference. Full article

The global energy landscape is undergoing a low-carbon, diversified, and high-efficiency transition, creating an urgent need to develop intelligent operation and maintenance (O&M) technologies for critical nuclear power equipment to boost plant economic efficiency. As the core “heart” component of the primary loop, the reactor coolant pump (main pump) must meet extremely stringent reliability criteria to ensure safe and stable operation of nuclear facilities. Existing remaining useful life (RUL) prognostics for main pumps mostly output deterministic point estimates; they fail to quantify predictive uncertainties and cannot provide credible risk intervals to support maintenance decision-making. To fill this research gap, this study first performs coupled thermomechanical failure simulations for three vulnerable main pump components: the rotor shaft assembly, double-cone sealing structure, and motor shielding sleeve. Simulation results are validated via tests on a full-scale main pump prototype bench to extract sensitive degradation characteristic parameters. Accordingly, a hybrid Bayesian Transformer-LSTM prognostic framework is proposed for main pump RUL prediction with built-in uncertainty quantification. Data augmentation is utilized to expand multi-source degradation datasets of main pumps. The Mahalanobis distance is employed to build component-level health indicators (HIs), and a cloud barycenter weighted evaluation method fuses these sub-component HIs into a unified system-level comprehensive health index (CHI). Using the fused CHI as model input, the Bayesian Transformer-LSTM architecture incorporates probabilistic fully connected layers to simultaneously capture local time-series fluctuations and long-term global degradation trends, enabling joint RUL regression and uncertainty quantification. A full-scale main pump prototype from an in-service nuclear power plant is used to validate the multi-source data fusion strategy. Quantitative evaluation results show that the proposed method achieves a coefficient of determination R² = 0.997, root mean square error (RMSE) = 0.018, and prediction interval coverage probability (PICP) = 0.839. Comparative ablation experiments further confirm that the proposed model delivers outstanding fitting precision and reliable uncertainty quantification, enabling long-timescale full-lifecycle health characterization of main pumps. Full article

An active vibration-based methodology for structural health monitoring of polymer-matrix composites is presented, in which piezoelectric actuators excite resonant vibrations and the diagnostic information is carried by two nonlinear characteristics: the backbone curve and the amplitude-dependent logarithmic decrement. Both are described by a single elastoplastic model of the Iwan (microplasticity) type with a power-law distribution of yield thresholds. The two characteristics share a common power-law exponent, and the model predicts a parameter-free ratio between the modulus defect and the hysteretic intensity. The four parameters are identified by a joint Bayesian fit. The methodology is applied to two carbon-fiber-reinforced polymer objects: a cantilever beam with a symmetric stacking sequence (three modes, 87 to 1431 Hz) and a plate strip with an unsymmetric one (two modes near 34 and 203 Hz), each tested intact and after controlled local damage. The measured ratio reproduces the prediction within 4 to 12%; whereas, the fundamental plate mode reveals a non-frictional, matrix-dominated dissipation. Local damage increases the hysteretic intensity 1.4 to 2.3 times and the modulus defect up to 2.7 times, while the resonant frequency changes by less than 0.8% and the background decrement remains nearly unchanged, giving a compact damage signature with minimal baseline requirements. Full article

Normalizing-flow Markov chain Monte Carlo (MCMC), such as flowMC, augments local moves with a learned global flow proposal; a natural reliability idea is to pool samples from several such samplers. On 6 targets in 20 and 50 dimensions, a 5-member diverse flowMC ensemble appeared to reduce the average marginal Jensen–Shannon (JS) distance by about 18% relative to a single flowMC run. Matching the returned sample counts—our “equal budget”: wall-clock costs differ and are reported separately—reverses this verdict: the ensemble draws five times as many samples, and even a perfect sampler’s histogram JS estimate has a closed root-mean-square finite-sample floor $\sqrt{c(1/N+1/M)}$, with c a fitted coefficient set by the binning, which explains the apparent gain almost entirely. At a matched 10,000-sample budget, a single flowMC run matches or beats this ensemble on all eleven configurations and runs about six times faster. Applied to exact draws, the uncorrected reweight-and-resample aggregation reproduces about 93% of the ensemble’s elevation above the floor, separating a genuine law shift (coverage and tempering reweighting) from an estimator-level loss (resampling). On two real Bayesian posteriors scored against long NUTS references, uniform pooling improved on the sample-count-matched single run under marginal JS, energy distance, and ${\mathrm{MMD}}^2$ in every recorded run (a descriptive comparison at three and two repetitions), while the aggregation’s deficit is confined to the biased marginal metric. We recommend matched sample counts and floor reporting for histogram divergences, unbiased joint metrics alongside, and uniform pooling instead of uncorrected reweighting and resampling. Full article

Extreme disasters can simultaneously damage distribution equipment and communication links, preventing control centers from promptly acquiring complete energization information for critical nodes. This study formulates joint critical-node state identification in a fixed post-disaster radial topology as exact maximum a posteriori (MAP) inference in a tree-structured Bayesian network and proposes a path-cached branch-and-bound search (PCBS). PCBS combines admissible upper bounds for partial assignments, posterior-state discriminability ordering, bounded-width initial candidate construction, branch-and-bound pruning, and rollback-enabled path caching without changing the probabilistic model, candidate space, or MAP objective. Across 200 random scenarios on the IEEE 33-bus system, PCBS returned exactly the same MAP solutions and joint probabilities as exhaustive enumeration. On the IEEE 85-bus system with 14 critical nodes, the runtime speedup reached 28.3. When the evidence-quality score increased from 0.24 to 0.82, state accuracy rose from 78.30% to 92.40%, while runtime decreased from 0.714 s to 0.513 s. On the 300-node radial network, PCBS completed inference in 1.13 s on average with 30 critical nodes and 2.46 s with 60 critical nodes, while maintaining approximately 86.5–86.7% state accuracy. Hardware-in-the-loop experiments on a shipboard power system achieved 85.00% mean accuracy with a 1.31 s mean end-to-end inference time. Full article