Search Results (513)

Background/Objectives: Comparative effects of oral second-generation H1-antihistamines vary by rhinitis category, outcome, and treatment period. We assessed evidence structure, estimability, and differentiation among regimens. Methods: PubMed, Embase, Web of Science Core Collection, CENTRAL, WHO ICTRP, ClinicalTrials.gov, and the EU Clinical Trials Register were searched on 1–3 July 2026 for records through 30 June 2026. Eligible studies were natural-exposure randomized trials. Continuous outcomes were analyzed using covariance-aware generalized least squares, and binary harms were analyzed using Bayesian arm-level models. RoB 2, CINeMA, and ROB-MEN were applied in duplicate. Registration was retrospective. Results: Sixty-one reports generated 66 report–randomization entries from 64 cohorts (34,831 participants); 35 model-ready randomizations informed nine structures. In seasonal allergic rhinitis, six of seven TNSS estimates, two of three ocular estimates, and four RQLQ estimates favored active treatment. Any-adverse-event estimates were imprecise. Under the primary prior, cetirizine yielded a WDAE OR of 0.25 (95% CrI 0.08–0.66), but predictive classification was prior-sensitive. Of 34 continuous active–active comparisons, seven were directly informed and 27 indirect-only. All 147 estimable binary active–active contrasts were reported as secondary derivations: 141 CrIs included one and six excluded one, all indirect-only and not formally appraised with CINeMA. Six of 14 regimen-level nodes contributed only to tolerability structures, precluding balanced regimen-specific benefit–harm assessment. All 89 estimable comparisons within the defined CINeMA appraisal scope were rated very low; two non-estimable effects were not evaluable. Conclusions: Predominantly placebo-anchored and sparse evidence established neither a reliable regimen hierarchy nor therapeutic equivalence. Findings were hypothesis-generating, and somnolence remained an evidence gap. No external funding supported conduct; NEXUS PEOPLE, S.L. funded the APC. OSF registration was retrospective: doi:10.17605/OSF.IO/DPT5N. Full article

This paper introduces a shared gamma frailty Birnbaum–Saunders model for clustered lifetime data under progressive Type-II censoring. The frailty term acts on the scale parameter and accounts for unobserved variation among clusters. Likelihood-based and Bayesian formulations are developed, and posterior estimation and prediction are carried out using a Metropolis-within-Gibbs algorithm. A simulation study considers different shape parameters, frailty levels, numbers of clusters, and censoring schemes. The proposed method estimates the shape parameter accurately and gives generally satisfactory results for the frailty variance. Increasing the number of clusters improves estimation, and the frailty model gives lower prediction errors in almost all valid comparisons with the ordinary BS model. A real-data application to kidney catheter infection times illustrates the shared-frailty component of the proposed model. Full article

Non-life insurance pricing is forward-looking, yet its principal cost signal, reported claims, is delayed by the occurrence-to-reporting process. The rating cell is treated as a stylised, homogeneous unit without renewal, lapse, expiry, or cohort dynamics; a fully annual-contract formulation would require these dynamics to be modelled jointly with the risk and claims processes. This paper develops a partially observed risk-sensitive control framework in which premium-sensitive exposure generates claims in a latent risk regime, while unreported claims form an atomic population governed by an age-structured transport equation. The numerical instance solved and validated in this research restricts the general model to a memoryless (one-phase) reporting process for tractability. It is best matched to lines with predominantly short-to-medium reporting tails, rather than to the most extreme long-tailed liability or cyber exposures the general model is designed to eventually accommodate. A finite-state reporting reservoir and nominal Bayesian filter provide the decision state, and compound Poisson–Gamma loss enters an entropic Bellman recursion through a closed-form exponential-tilting identity, checked against an independently coded Bellman-residual test. Every dynamic policy is benchmarked against alternatives matched at the same risk-sensitivity parameter and evaluated using common random numbers. This is a general feature of the results, not a single statistic: $C{E}_{0.8}$ is a standardised yardstick applied uniformly across strategies, while $C{E}_{\gamma }$ at the policy’s own $\gamma$ is what that policy actually optimises, and the two need not agree. In 3000 out-of-model paths at $\gamma$ = 1.2, the delay-aware dynamic policy increases mean profit by EUR 0.148 million and a standardised ${\gamma }_{\mathrm{eval}}$ = 0.8 certainty equivalent by EUR 0.034 million relative to a matched static price. Its fifth percentile and TVaR 5%, by contrast, are lower by EUR 0.056 and 0.078 million. At the policy’s own optimisation level, however, the ${\gamma }_{\mathrm{eval}}$ = 1.2 certainty-equivalent difference is EUR −0.006 million, with a 95% interval reaching zero. The dynamic policy, therefore, does not clearly outperform the matched static price on the exact objective it was optimised to maximise. Matched comparisons attribute EUR 0.039–0.043 million of mean profit to reporting-delay modelling and EUR 0.017 million to dynamic continuation. Separating state observation from transition-law knowledge attributes EUR 0.161–0.182 million to observing the regime exactly, and a small, sign-changing EUR −0.010 to +0.003 million to knowing the true transition law itself. Across an eight-scenario misspecification stress suite, the dynamic policy’s mean-profit advantage over the matched static price is directionally robust in seven of eight scenarios, but it reverses sign under a +30% true-severity shock, indicating that this advantage is sensitive to substantial severity misspecification specifically. Grid, Bellman-residual, and out-of-model filter diagnostics indicate that state reconstruction is economically primary, while entropy risk sensitivity is a secondary overlay whose apparent benefit depends materially on which certainty-equivalent level and evaluation model are used to judge it. Full article

Multilingual online reviews combine ordinal ratings, asymmetric positive and negative evidence, sparse attribute occurrence, and substantial cross-country imbalance. This study presents an integrated inferential framework. Signed topic scores are converted to within-country rank intensities, and country-specific cumulative-logit models distinguish positive and negative occurrence baselines from their corresponding intensity contrasts. The two intensity contrasts are then synthesized jointly through a bivariate Bayesian normal-normal random-effects model that retains their within-country covariance. The primary analysis uses all 30,042 reviews observed in the common 2019–2024 window; the complete 85,373-review corpus and repeated country-capped samples are sensitivity analyses. Separating the two occurrence baselines improves summed AIC from 32,347.7 to 32,167.8, while a transformation-by-function comparison shows that natural splines improve AIC and quadratic-weighted agreement. The linear-rank model is retained to provide comparable scalar intensity contrasts. Targeted partial proportional-odds fits substantially improve in-sample AIC in China and Japan but leave repeated-validation performance and all nine average effect directions essentially unchanged. A secondary semantic mapping audit agrees with 30 of 31 topic assignments. Simulation results show generally adequate interval coverage but reduced Kano-state accuracy under small K, sparse occurrence, and proportional-odds violations. The findings therefore support tiered, uncertainty-aware prioritization rather than a deterministic global ranking. Full article

Leptoglossus chilensis is a native South American coreid bug of economic and quarantine relevance whose chemical ecology remains undescribed. In this exploratory study, we characterised adult volatile and cuticular chemistry using dynamic headspace sampling (DHS), cuticular wash (CW), solid-phase microextraction (SPME), supercritical fluid extraction (SFE), and quantified electroantennographic (EAG) dose–responses in males and females in relation to a panel of eight literature-informed compounds (hexanal, hexyl acetate, hexyl formate, β-caryophyllene, (E)-β-farnesene, ocimene, benzyl alcohol and 2-phenylethanol). EAG responses were evaluated at six concentrations using a Bayesian hierarchical four-parameter Hill model. Across the four methods, 128 compounds were detected, of which 69 met a ≥20% detection-frequency threshold and were retained as the representative profile. Of these, hexanal, hexyl acetate, hexyl formate, and β-caryophyllene were detected in L. chilensis extracts, the latter only in male DHS collections; this compound is more likely to be a host plant volatile than an insect-produced signal. All eight compounds elicited concentration-dependent antennal responses in both sexes. Among these four compounds, β-caryophyllene yielded the lowest fitted EC₅₀ in both females (9 µg mL⁻¹ [90% CrI: 5–16]) and males (9 µg mL⁻¹ [90% CrI: 5–15]), although curves remained unsaturated; because delivered antennal dose was not measured and volatility differs substantially among the tested compounds, this cannot be interpreted as evidence of greater antennal sensitivity to β-caryophyllene specifically, and cross-compound EC₅₀ comparisons throughout this study are restricted to this caveat. All four Hill parameters were identifiable in both sexes for benzyl alcohol, (E)-β-farnesene, hexyl formate, and 2-phenylethanol. These results provide the first chemical and peripheral olfactory reference for L. chilensis and identify targets for behavioural and field evaluation. Full article

Oil-exporting economies are notoriously sensitive to external shocks, but the importance of current oil revenue absorption for macroeconomic shock transmission has not been explored thoroughly. This paper constructs a stylized DSGE model to analyze how macroeconomic shock propagation differs under alternative degrees of current oil revenue absorption. The model combines calibration to resemble Kazakhstan with Bayesian estimation. Six absorption rates are chosen that form three pairwise comparisons: 10% vs. 90%, 20% vs. 80%, and 40% vs. 60%. The model is analyzed under external oil demand, oil price, foreign output, total factor productivity, labor preference, and monetary policy shocks. Simulation impulse responses indicate that increased current absorption dramatically magnifies shocks that impact oil export income directly. External oil demand and oil price shocks exhibit strong responsiveness to changes in the absorption rate, while impulse responses to productivity, labor preference, monetary policy, and foreign output shocks display relatively little sensitivity to the absorption regime. For example, under the 10–90% comparison, the first period output response to an external oil demand shock increases from 3.195 to 19.481, while associated responses to a productivity shock cluster closely together with uncertainty bands that overlap extensively. Intermediate absorption comparisons maintain the same shock-specific conclusion while differences between regimes diminish as absorption rates get closer. This evidence suggests that the macroeconomic vulnerability of oil-exporting economies is a function not only of whether a shock is external, but also if it affects oil export income and has the capacity to interact with the oil revenue smoothing mechanism. These results lend support to intertemporal smoothing as a possible method to alleviate oil-income shock exposure in the short-run, but do not suggest an optimal level of current absorption. 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

Vegetation–hydrothermal trend patterns in semiarid mountains are inherently multidimensional across altitudinal gradients, requiring probabilistic frameworks to characterize overlapping vegetation–hydrothermal trend patterns. We develop a Gaussian mixture model (GMM)-based framework to classify vegetation trend regimes from multidimensional trend variables and to characterize their climate–regime associations across elevation gradients in the semiarid Al Baha region, Saudi Arabia. Landsat time-series data (2014–2025) were used to derive trends in the Normalized Difference Vegetation Index (NDVI), normalized difference water index (NDWI), and land surface temperature (LST) using the Mann–Kendall test. These standardized trend variables were integrated within a probabilistic GMM framework, with candidate models evaluated using Bayesian information criterion (BIC), Integrated Completed Likelihood (ICL), classification entropy, resampling-based partition stability, and held-out predictive performance. Although BIC favored the eight-component VVV model, the six-component solution was retained as a balanced intermediate-complexity representation based on partition reproducibility, near-optimal predictive performance, classification ambiguity, and parsimony. The results reveal a pronounced elevation-dependent reorganization of vegetation–hydrothermal trends, characterized by High Relative LST Trend dominance at low elevations (~44%), Mixed Trend State at mid elevations (~40.8%), and Low Relative NDWI Trend at higher elevations (>48%), with High Relative LST Trend becoming negligible at the highest altitudes. This spatial organization showed a moderate association with elevation (Cramér’s V = 0.299; χ² = 74,265.54, p < 0.001), with statistical significance interpreted cautiously given the large, spatially autocorrelated sample. Climate–regime analysis further reveals that precipitation variability exhibits the strongest association at mid elevations, whereas temperature shows stronger associations at both low- and high-elevation extremes. Cross-sensor comparison with VIIRS showed substantial classification consistency after class-label alignment (overall agreement ≈ 0.76; κ ≈ 0.64). These findings demonstrate systematic variation in vegetation–hydrothermal trend regimes and their climate associations along elevation gradients. The proposed GMM-based framework provides a scalable and uncertainty-aware approach for assessing climate–vegetation associations in semiarid mountain systems and other data-scarce dryland environments. Full article

This study characterized the long-term changes in reported farm-level fowl typhoid occurrence in the Republic of Korea from 2000 to 2024. Farm-level notification records (n = 1495) were obtained from the national livestock disease database. Annual counts were analyzed on a log(count + 1) scale using segmented linear regression. The number of breakpoints was selected by Bayesian information criterion (BIC) comparison of candidate segmented models, and BIC favored a two-breakpoint model. Breakpoints were estimated at 2009.0 (95% CI, 2004.7–2013.3) and 2016.5 (95% CI, 2014.1–2019.0). Phase 1 (2000–2009) declined significantly (annual percent change [APC], −16.3%; 95% CI, −25.9 to −5.5), Phase 2 (2009–2016) showed no significant change (APC, 3.7%; 95% CI, −10.3 to 20.0), and Phase 3 (2016–2024) showed an accelerated decline (APC, −28.0%; 95% CI, −34.9 to −19.8). Province-specific post-2016 slopes were not associated with cumulative provincial HPAI events during 2014–2024 (Spearman’s ρ = −0.143, p = 0.736). The post-2016 acceleration temporally overlapped with the 2016–2017 H5N6 HPAI epidemic, but the study did not measure the contemporaneous interventions or denominators needed for causal attribution. 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

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

Accurate calibration of the Preston–Tonks–Wallace (PTW) model is a nonlinear, multimodal optimization problem. Root mean square error (RMSE) is the sole optimization objective, with average absolute relative error (AARE) used only as a diagnostic. The primary comparison jointly calibrates one seven-parameter vector across all temperatures within each fixed strain-rate group; the 0.1, 3000, and 5000 s⁻¹ groups are calibrated separately. A secondary transfer assessment imposes the stricter constraint of one unchanged vector across all 11 temperature-strain-rate conditions. The adaptive GA-BO strategy combines diversity-dependent mutation, periodic and stagnation safeguards, elite-envelope Bayesian optimization (BO), and BO-to-population reinjection. Genetic algorithm (GA), BO, and GA-BO each receive exactly 1000 constitutive-model evaluations and 20 paired seeds. In the primary comparison, GA-BO gives median RMSE values of 8.06, 7.94, and 21.69 MPa and significantly improves on GA at all three rates, while remaining statistically comparable to BO and requiring substantially less serial time in the present implementation. The shared-vector assessment gives a GA-BO median global RMSE of 45.54 MPa and quantifies the accuracy-transfer trade-off created by enforcing one vector over the full rate range. Bootstrap, held-out-temperature, ablation, and one-factor setting analyses further quantify parameter coupling, calibration-transfer uncertainty, component contributions, and setting robustness. The resulting workflow therefore improves the accuracy-efficiency balance of reproducible PTW calibration while explicitly reporting the parameter-transfer cost across rate regimes. Full article

Generative data augmentation has been widely explored in Wi-Fi fingerprint-based indoor localization to reduce the cost of dense radiomap construction, with many studies reporting substantial localization improvements. However, these comparisons typically rely on default or weakly optimized baseline regressors, making it difficult to determine whether reported gains reflect genuine synthesis quality or merely compensate for suboptimal baselines. In this paper, we systematically investigate under which conditions generative augmentation is actually justified. We fine-tune four widely used localization regressors—kNN, SVR, XGBoost, and DNN—using Bayesian hyperparameter optimization and establish strong non-augmented baselines across radiomaps with controlled levels of spatial sparsity, constructed via farthest-point sampling. We then train five representative generative models—VAE, GAN, DDPM, DiT, and TDPM—within a unified augmentation pipeline that includes quality filtering and pseudo-labeling, and benchmark them against these baselines. Using two publicly available datasets, we show that none of the generative models consistently outperforms a non-augmented, fine-tuned baseline regressor such as XGBoost or kNN, across a wide range of sparsity levels. We further show that these conclusions are robust to three potential confounders: various proportions of synthetic data, the choice of localization regressor (ruling out circularity with the pseudo-labeling model), and the dataset itself, since the findings on the first dataset replicate on a second, structurally different building. These findings suggest that reported augmentation benefits in prior work may partly reflect under-optimized baselines rather than genuine synthesis quality, and that generative augmentation should be treated as a conditional last resort rather than a universal improvement strategy. Full article

Real data such as road traffic mortality rates and lifetime observations are often zero-inflated and right-skewed. The zero-inflated two-parameter Rayleigh (ZITR) distribution is employed to model such data in this study. The coefficient of variation (CV) is a statistical measure that is used to quantify the relative dispersion of a population, by comparing the standard deviation with the mean. It is widely used to evaluate variability and facilitate comparisons among datasets with different scales or measurement units. This study develops and evaluates seven methods for constructing confidence intervals for the single CV of the ZITR distribution. Three proposed approaches, including Bayesian Markov chain Monte Carlo (MCMC), Bayesian highest posterior density (HPD), and approximate normal (AN) methods, are compared with three existing approaches: generalized confidence interval (GCI), percentile bootstrap (PB), and bootstrap with standard error (BS). Monte Carlo simulations are employed to assess the efficacy of these methods in terms of expected length (EL) and coverage probability (CP). The simulation results show that the HPD method gives acceptable CP with shorter interval lengths than other methods. Moreover, the proposed methods are illustrated with road traffic mortality rates per 100,000 population collected in January 2026 from the Phichit, Suphan Buri, and Prachuap Khiri Khan provinces in Thailand. The results indicate the applicability of the proposed methods for analyzing zero-inflated and right-skewed data in this real-data example. Full article

The jump height of overhead transmission lines after ice shedding is closely related to electrical clearance, and structural safety. For different conductor parameters, span lengths, horizontal stress, and icing conditions, transient finite-element analysis can provide detailed dynamic responses, but repeated simulations are not convenient for fast engineering assessment. In addition, a deterministic prediction model only gives a single jump-height value, and the reliability of this value is difficult to judge when the input condition is close to the boundary of the sampled range. In this study, a Dimensionless-Group-Guided Bayesian Neural Network (DG-BNN) is developed to predict the ice-shedding jump height and estimate the associated uncertainty. The original physical variables are first transformed into seven dimensionless Pi-groups according to Buckingham Pi dimensional analysis. These variables describe the main effects of geometry, mass distribution, stress state, and ice shedding in a compact form. The network is trained through a two-stage procedure. A heteroscedastic regression model is first obtained with a trend-regularization term based on a fixed second-order polynomial, and then the deterministic layers are converted into Bayesian layers for probabilistic inference. Monte Carlo sampling is used to calculate the predictive mean, epistemic uncertainty, and aleatoric uncertainty. Temperature scaling is further introduced to adjust the prediction intervals on the validation set. The comparison with several regression models shows that DG-BNN can maintain accurate jump-height prediction while giving calibrated uncertainty information. On the FEM-generated test set, DG-BNN achieved an R² of 0.986, an RMSE of 0.958 m, and an MAE of 0.649 m. After temperature calibration, the coverage probability of the 90% prediction interval reached 89.96%. The model can therefore serve as a fast surrogate tool for ice-shedding response assessment, especially when the reliability of the predicted result needs to be considered. Full article