Search Results (496)

Because of the increasing negative effects of drought on sectors such as water resources, the economy and agriculture, there is a strong need to study drought and its projections. Given the importance of studying different scenarios regarding drought and climate change, this study presents an integrated modelling framework to generate future drought projections for the Western Black Sea Basin, Türkiye, under four Shared Socioeconomic Pathway (SSP) scenarios derived from the Coupled Model Intercomparison Project Phase 6 (CMIP6). Monthly precipitation data from seven General Circulation Models—ACCESS-CM2, CanESM5, CNRM-CM6-1, IPSL-CM6A-LR, MIROC6, MPI-ESM1-2-LR and MRI-ESM2-0—were statistically downscaled to 31 meteorological observation stations using a Multivariate Adaptive Regression Splines (MARS) approach, trained over the 1979–2014 period. Precipitation records from all 32 available meteorological stations were quality-controlled, and the 31 stations with continuous temperature records were retained for the full analysis, so that both indices could be computed at the same locations. Systematic biases in the downscaled outputs were subsequently corrected using the Quantile Delta Mapping (QDM) method, an essential step that simultaneously reduces distributional errors and preserves the future climate change signal. Both the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) were computed at 3-, 6- and 12-month scales, with distribution parameters estimated once over the 1979–2014 window of the bias-corrected model series and held fixed for 2015–2100 so that baseline and future statistics remain comparable. The two indices yield markedly different projections. Although the number of drought events falls, the share of months below the moderate-drought threshold at the 12-month scale rises from 16.5% in the baseline to 47.0% by 2061–2100 under SSP5-8.5 for the SPI and to 88.4% for the SPEI. All 217 station–GCM combinations show the SPEI as the drier index in every scenario and period. The reason is the temperature term: under SSP5-8.5, basin-mean precipitation changes by −20 mm yr⁻¹ by the late century while potential evapotranspiration rises by +192 mm yr⁻¹, so that about 91% of the change in the climatic water balance is attributable to evaporative demand rather than to rainfall. What changes is therefore not how often drought occurs but how long it lasts and how much deficit it accumulates. Projections under SSP1-2.6 indicate substantially less severe drought conditions than those under the higher-emission pathways at every station, although drought exposure still increases relative to the baseline. Full article

This study examines the impact of cassava producers’ participation in inclusive agribusiness models (IAMs) on allocative efficiency in Benin, providing new microeconomic evidence on smallholder market integration in Sub-Saharan Africa. Using a sample of 1167 farmers across seven departments, we combine Stochastic Frontier Analysis (SFA) with an Endogenous Switching Regression (ESR) model to estimate efficiency while correcting for observable and unobservable selection bias. The results reveal moderate average allocative efficiency (0.646), indicating that producers remain about 35 percentage points below the cost-minimizing input allocation. IAM participants display higher allocative efficiency (0.700) than non-participants (0.620). The ESR model yields a positive and statistically significant treatment effect: participation is associated with an increase of 0.150 points in allocative efficiency among beneficiaries (ATT), while the potential effect for non-participants is smaller (ATU = 0.060). These results are corroborated by an Inverse-Probability-Weighted Regression Adjustment (IPWRA) robustness check, which yields a closely comparable treatment effect (ATT = 0.132). This ATT–ATU asymmetry reveals treatment-effect heterogeneity and positive selection on gains, as producers with the largest expected efficiency gains are the most likely to participate. Within IAMs, ordinary cooperative membership and the existence of a sales contract are associated with higher allocative efficiency, whereas extension contact and meeting participation, although the strongest predictors of participation, are associated with lower efficiency among participants, pointing to time-allocation trade-offs. The findings offer actionable evidence for designing inclusive value chain policies targeting the most marginalized smallholders and addressing structural constraints limiting efficiency across producer groups. Full article

Accurate wind information is essential for offshore wind-resource assessment in the East China Sea, yet global reanalyses remain poorly constrained in this region. Using one year of multi-height (20–200 m) floating-lidar observations from the Shanghai Far-Sea Wind Demonstration Site, this study evaluates ERA5 wind performance, develops a machine-learning bias-correction model, and reconstructs a bias-corrected decadal (2016–2025) hub-height wind resource. ERA5 reproduces observed wind variability with moderate fidelity (Pearson R = 0.71) but systematically underestimates wind speed above ~50 m, with the bias intensifying with height (−0.96 m s⁻¹ at 200 m) and root-mean-square error (RMSE) rising from 2.78 m s⁻¹ at 20 m to 3.39 m s⁻¹ at 200 m. Wind-speed bias and RMSE peak at hub heights of 100–200 m in summer, while wind-direction mean absolute error (MAE) is largest in spring, reflecting seasonally dependent error structures. Among ten candidate models, a Transformer trained on 6 h sequences of ERA5 pressure-level winds and stability fields performed best, reducing the height-averaged wind-speed RMSE by 46% from 3.02 to 1.63 m s⁻¹, improving correlation to R = 0.92, and cutting wind-direction MAE from 30.3° to 12.4°. Joint residual learning of the u- and v-components preserved vectorial consistency in both speed and direction. Applied to the full 2016–2025 archive, the calibrated model reveals statistically significant positive trends in annual-mean wind speed at 150–200 m (0.69–0.75 m s⁻¹ decade⁻¹; Mann–Kendall p < 0.05), whereas the trends at 100 m (p = 0.11) and 130 m (p = 0.07) are positive but not statistically significant, with the Weibull scale parameter c generally increasing (with a minimum in 2019) while the shape parameter k remained near 2.0, indicating a shift toward higher characteristic wind speeds. Requiring only standard ERA5 fields at inference time, the framework is computationally efficient and well-suited to support offshore wind-resource assessment, yet further validation is needed for extension to annual energy production (AEP) estimation and power forecasting. Full article

Sea-level rise is a key indicator of climate change, increasing the vulnerability of the Mediterranean’s densely populated, socio-economically important coastlines. This study investigates Mediterranean sea-level variability and its physical components over 2005–2025. Multi-mission altimetry products, which retain inter-mission differences despite standard geodetic corrections, were cross-calibrated into a homogeneous time series and analyzed with harmonic Ordinary, Weighted, and Generalized Least Squares models, accounting for observational uncertainty and temporal correlation. Adopting the uncapped, maximum-likelihood-consistent correlated-noise variance as the primary convention, the final GLS altimetric trend is +3.277 mm/yr. The signal was decomposed into steric (thermosteric, halosteric) and manometric components, using GLORYS, ARMOR3D and Argo data for the steric terms and GRACE/GRACE-FO for the manometric term. GLS steric trends range from +1.445 to +2.149 mm/yr; the JPL manometric estimate, corrected for atmospheric loading on a common JPL/AVISO/LEGOS mask and estimated by GLS, is +1.318 mm/yr. Combining each steric and manometric estimate gives six budget-closure residuals from −0.623 to +0.514 mm/yr; correctly propagating their non-independence gives a grand-mean residual of −0.049 ± 1.616 mm/yr, statistically indistinguishable from zero (p = 0.98)—the budget closes within uncertainty. The altimetric trend and bias solution are independently validated against 47 GNSS-corrected Mediterranean tide gauges, giving a mean geocentric trend of +2.365 ± 0.263 mm/yr, consistent with the altimetric estimate (p = 0.49). Full article

ERA5 100 m wind speed provides long-term and spatially continuous information for regional wind-resource assessment, but its grid-scale representation may introduce terrain- and wind-regime-dependent biases in complex terrain. This study evaluated ERA5 using 459,630 valid hourly observation–reanalysis pairs from 64 wind masts and developed a segmented residual-correction framework incorporating wind-regime, terrain, location, and temporal predictors. Three statistical correction methods and four tree-based models were compared under identical training and validation samples. The main analysis used a stratified random holdout, supplemented by chronological and tower-level spatial holdouts and repeated high-wind experiments. In the random validation subset, raw ERA5 yielded R = 0.669, R² = 0.307, RMSE = 2.261 m s⁻¹, MAE = 1.664 m s⁻¹, and ME = −0.904 m s⁻¹. Random forest achieved the best paired hourly performance, increasing R to 0.850 and reducing RMSE and MAE to 1.433 and 1.078 m s⁻¹, respectively. Its RMSE reductions remained positive but decreased to 14.15% and 14.47% under chronological and spatial holdouts. Terrain relief was strongly associated with tower-level raw ERA5 errors, although its controlled inclusion produced only a modest additional RMSE reduction of approximately 0.7%. Pooling the >9 m s⁻¹ training tail improved high-wind stability and outperformed a separately trained >12 m s⁻¹ model in 17 of 20 experiments. Random forest was preferable for paired hourly reconstruction, whereas quantile mapping more closely reproduced pooled theoretical wind-energy indicators. These results show that ERA5 bias correction should be selected according to the intended application and evaluated using complementary temporal, spatial, distributional, and high-wind diagnostics. Full article

High measured efficiency can indicate a well-run container port or one operating with little spare capacity; an efficiency score alone does not distinguish the two. This study examines port efficiency after finite-sample bias correction and alongside evidence of queueing pressure and phase-II normalized input slack measured relative to the estimated frontier. Using double-bootstrap data envelopment analysis for 18 major global container ports, with annual container throughput as output, we estimate bias-corrected efficiency and its conditional association with two AIS-derived indicators: berthing time per TEU and waiting time per vessel. In the baseline specification, longer berthing time per TEU is significantly associated with greater input inefficiency, whereas waiting time is not significant, indicating that efficiency and congestion capture distinct operational dimensions. This service-time association remains positive but loses statistical significance when berth time is normalized by called vessel capacity rather than by throughput, so it is reported as sensitive to the choice of normalization. Joint analysis with phase-II maximal normalized input slack identifies high-efficiency ports with substantial queues despite zero measured maximal normalized slack and lower-efficiency ports where input excess coexists with congestion. These findings show that neither efficiency nor infrastructure slack alone identifies operating headroom for absorbing disruption. The study provides bias-corrected efficiency measurement and bootstrap inference, distinguishes berth-side service productivity from queueing pressure, and offers a static operational diagnostic, scalable across ports, that characterizes operating conditions relevant to absorptive capacity without treating efficiency or congestion as resilience itself. Full article

PCHZ-SIS (permutation-centered tie-aware Henze–Zirkler sure independence screening) is a specialized marginal screening method for mixed data containing continuous and ordered–discrete predictors with extensive ties. The method combines tie-aware mid-rank Gaussianization, exact analytical computation of the conditional permutation means of the normalized Henze–Zirkler statistic, and a randomized incomplete-pair approximation. For the signed centered functional, an exact zero conditional expectation under the permutation null is established. For the incomplete-pair implementation, the uniform approximation error is of order log pM; for the oracle full-pair functional with fixed population transforms, the finite-sample bias of order 1n and stochastic error of order log pn are controlled separately. The empirical transform error is isolated as a separate term and bounded by a finite-sample DKW/Lipschitz argument; whether it vanishes asymptotically depends on the clipping regime. Preservation of the active set in the top-d ranking is guaranteed only under an explicit marginal separation condition, when the signal gap exceeds the combined statistical, transformation, and computational errors. In Monte Carlo experiments with n=200 and p=500, PCHZ-SIS achieved a mean TPR of 0.86 in the mixed/tied scenario versus 0 for HZ–common-clip, while DC-SIS remained the strongest general nonlinear comparator. Field assessment on 30 Tengiz wells showed that PCHZ-SIS reduced inflated HZ scores for several low-cardinality variables but did not improve downstream ridge performance relative to HZ–common-clip. PCHZ-SIS is therefore positioned as an HZ-specific finite-sample correction for severe ties rather than as a universal replacement for modern screening methods or a fully calibrated inferential testing procedure. A separate 200-replication confirmation experiment with n=200 and p=500 reproduced the severe ties finding using the exact published Xue–Liang truncation: PCHZ-SIS achieved a mean TPR of 0.885 versus 0 for HZ-Xue–Liang-clip; DC-SIS remained the strongest general nonlinear comparator (TPR 1.000), while SWD-SIS yielded TPR 0.425. A persistence-controlled Tengiz residual benchmark further evaluates the method; PCHZ-SIS improves over HZ-Xue–Liang-clip on this stricter endpoint without implying universal superiority over distance- or rank-based screens. An independent cross-domain field assessment on 180 complete haul-truck cycles further showed that PCHZ-SIS reduced downstream MAE from 1.598 ± 0.110 HEP percentage points for the two uncentered HZ clipping variants to 1.534 ± 0.088, while Spearman-SIS and DC-SIS remained slightly stronger (1.505 ± 0.104). Full article

Static groundwater vulnerability assessments assume time-invariant inputs—an assumption hard to defend under climate change, when depth to water and net recharge are precisely the parameters that evolve with the forcing. This study develops a time-varying, multi-model framework for groundwater vulnerability assessment and applies it to the Almyros coastal aquifer (Thessaly, Greece) through the DRASTIC index. Time-varying depth-to-water and net-recharge fields are simulated for a baseline (1991–2018) and two future periods (2031–2060, 2071–2100) using an Integrated Modelling System driven by 19 bias-corrected regional climate model realizations under two emission scenarios (RCP4.5, RCP8.5); the remaining DRASTIC parameters are held time-invariant. Validation at 73 monitoring-well locations against IMS-simulated baseline nitrate concentrations, with the IMS having been calibrated against observed nitrate measurements, yields significant correlations for all 19 realizations (r = 0.569–0.742, p < 0.001). The best-fit realization reveals a non-monotonic trajectory of the combined High and Very High vulnerability area—54.2% at baseline, 36.6% at mid-century, 47.3% by 2071–2100 under RCP8.5. Formal partitioning of the climate-driven variance attributes 97.7–99.6% to inter-model spread and finds the between-scenario share statistically unresolvable, so climate model choice dominates within-scenario uncertainty. Because DRASTIC responds directly to depth to water, the projected index falls where water tables deepen and rises where they are shallow; static assessments, fixed at the baseline, therefore systematically overestimate future vulnerability where water tables decline and underestimate it where they rise. The framework is portable to other index-based methods and delivers climate-aware, empirically constrained projections for adaptive groundwater management. Full article

Background: Static single-time-point assessment of carcinoembryonic antigen and carbohydrate antigen 19-9 cannot capture postoperative temporal patterns. We evaluated whether their joint longitudinal trajectories were associated with survival after curative gastrectomy while explicitly addressing the time-dependent bias created when trajectory groups are defined using measurements obtained after surgery. Methods: We analyzed 208 patients with serial carcinoembryonic antigen and carbohydrate antigen 19-9 measurements. Five-time-point trajectories were retained as a descriptive and exploratory analysis. Primary prognostic inference used a 12-month landmark analysis restricted to patients alive and recurrence-free at 12 months with at least three observed paired measurements from the preoperative, 1-month, 6-month, and 12-month time points. Trajectories were re-derived using only pre-landmark information. Overall survival was defined by all-cause mortality. In leakage-controlled fivefold cross-validation, imputation, transformation, cluster selection, trajectory assignment, and model fitting were performed within each training fold. Results: The 12-month landmark cohort included 164 patients and yielded four joint trajectory groups: G1, n = 60; G2, n = 41; G3, n = 32; and G4, n = 31. After adjustment for age, sex, TNM stage, differentiation, tumor site, postoperative chemotherapy, and baseline log-transformed carcinoembryonic antigen and carbohydrate antigen 19-9, G4 was associated with higher point estimates for overall survival and disease-free survival but did not reach statistical significance, with hazard ratios of 1.81 and 1.90, respectively. Adding the landmark trajectory classification increased the apparent C-index from 0.689 to 0.710 for overall survival and from 0.711 to 0.723 for disease-free survival, while likelihood-ratio tests for incremental model fit were not significant. In leakage-controlled cross-validation, the Cox model with CTS4 achieved an out-of-fold C-index of 0.589 and a 24-month post-landmark AUC of 0.635, compared with 0.614 and 0.643 for logistic regression using the same inputs. The original five-time-point surgery-indexed analysis showed stronger associations but was retained only as exploratory because the exposure incorporated future follow-up information. Conclusions: Joint carcinoembryonic antigen and carbohydrate antigen 19-9 trajectories may contain prognostic information, but the association was materially attenuated after correction for exposure timing. These findings should therefore be considered exploratory and hypothesis-generating until prospective external validation is available. Full article

Although Global Navigation Satellite System (GNSS) observations are verified to suffer from mixed additive and multiplicative errors, current methodologies still rely heavily on conventional additive error models (AEMs). Furthermore, existing research on mixed additive and multiplicative error models (MAMEMs) is largely confined to least squares frameworks and simulated scenarios. To address this, this study explores parameter estimation for MAMEMs using the robust L1-norm approach. Four novel algorithms are proposed: the L1-norm-based Gauss Theorem Algorithm (L1-Gauss), Linear Programming Algorithm (L1-LP), Relaxation Method (L1-RM), and Weighted Relaxation Method (L1-WRM). Among them, L1-WRM considers observation weights, while the other three algorithms treat observations as equal weights. Using real-world GNSS datasets, the proposed algorithms are compared with least squares (LS), weighted least squares (WLS) and M-robust weighted least squares (MWLS) under AEMs, as well as the bias-corrected WLS (bcWLS) estimator specifically designed for MAMEMs. Experimental results demonstrate that: Statistically significant differences exist in accuracy among the eight algorithms. In scenarios affected by multipath or non-line-of-sight (NLOS) effects, the accuracy of the bcWLS outperforms that of the WLS, and the accuracy of the L1-WRM outperforms that of the MWLS, with robust algorithms achieving superior accuracy. In scenarios without multipath or NLOS effects, the accuracy of bcWLS is virtually consistent with WLS, showing a difference in 3D Mean Error of only about 2 cm. Similarly, the accuracy of L1-WRM is virtually consistent with MWLS, with a 3D Mean Error difference of only about 8 cm; in this case, non-robust algorithms outperform robust algorithms. Regarding single-epoch execution time, the maximum computation time across the five MAMEMs algorithms is 95.5939 ms, fully satisfying the requirements for 1 Hz real-time processing. These findings validate the practical application value of the proposed algorithms in GNSS pseudorange relative positioning. Full article

This study presents a comparative analysis of regression models for predicting a synthetic corrosion defect severity (CR) indicator in pipeline systems. The investigated dataset contains 10,292 observations, eight input features, and a synthetic target variable. Fifteen models and configurations were compared: Dummy Regressor (Dummy), Linear Regression (LR), Ridge Regression (Ridge), Lasso Regression (Lasso), Elastic Net (EN), second- and third-degree polynomial regression (Poly2 and Poly3), k-Nearest Neighbors (KNN), Support Vector Regression (SVR), Extra Trees Regressor (ETR), Gradient Boosting Regressor (GBR), Histogram-based Gradient Boosting Regressor (HGBR), AdaBoost Regressor (ABR), Multilayer Perceptron (MLP), and CatBoost Regressor (CBR). The data were split into an 80% development set and a prespecified 20% held-out internal test set. Model selection and hyperparameter tuning were performed without accessing the test set using repeated nested cross-validation, with five outer folds repeated five times and five inner folds. The lowest mean root mean square error (RMSE) in the outer cross-validation was achieved by second-degree polynomial regression, with an RMSE of 0.019557 and a standard deviation of 0.001796; the mean absolute error (MAE) was 0.003762, and the coefficient of determination (R²) was 0.721530. However, its advantage over SVR was not statistically significant after Holm correction (adjusted p = 0.0662). On the prespecified held-out internal test set, Poly2 achieved an RMSE of 0.021454, an MAE of 0.003967, and an R² of 0.679304. In the highest CR decile, the error increased to an RMSE of 0.050622, the bias was −0.017468, and R² decreased to −1.372106, indicating systematic underestimation of high CR values. Permutation importance analysis identified carbon dioxide (CO₂) content, gas production rate, pressure, water production rate, basic sediment and water (BSW), and oil production rate as the most influential features. These findings are limited to a computational experiment conducted on synthetic data and require external validation using actual corrosion measurements. Full article

Reliable soil moisture information is essential for agricultural drought warning, but tropical smallholder regions often lack ground networks for validating satellite products. This study evaluates the Cyclone Global Navigation Satellite System (CYGNSS) Level 3 soil moisture product in Guinea savanna agriculture over Benue State, Nigeria, from 2021 to 2023 using reference-free collocation diagnostics. Extended Triple Collocation (ETC) was applied to CYGNSS, the Soil Moisture Active Passive (SMAP) Enhanced Level 3 product, and European Centre for Medium-Range Weather Forecasts fifth-generation land reanalysis (ERA5-Land) 31-day centered anomalies to estimate model-derived correlation with a latent soil moisture anomaly signal, estimated error standard deviation, and signal-to-noise ratio (SNR). A covariance-pathway Quadruple Collocation (QC) analysis then introduced the European Space Agency Climate Change Initiative active microwave soil moisture product (ESA CCI ACTIVE) as a fourth, structurally distinct product to test whether the CYGNSS–SMAP pair exhibited significant direct error correlation. The regional ETC configuration gave CYGNSS an estimated latent correlation of $r=0.425$, an estimated error standard deviation of $0.036{\mathrm{m}}^3{\mathrm{m}}^{-3}$, and an SNR of $-6.56$ dB. In the common quadruplet sample, the SMAP-inclusive CYGNSS estimate was $r=0.423$, whereas the SMAP-independent configuration gave $r=0.386$, indicating a modest configuration-dependent inflation of $\Delta r=0.0368$. However, the QC cross-error correlation was not statistically significant ($r_{\epsilon }=0.0007$, 95% confidence interval (CI) [$-0.0270$, $0.0283$]). Performance was weakest under dry soils $(r=0.331)$, where drought detection is most important. Harmattan diagnostics showed that dry-season ETC failure was associated with reduced anomaly variance and selective CYGNSS decoupling from SMAP and ERA5-Land rather than numerical ill-conditioning alone. Skill was higher over cropland $(r=0.447)$, shrubland or grassland $(r=0.455)$, and moderate precipitation conditions $(r=0.630)$, but lower over tree cover $(r=0.342)$. These findings indicate that uncorrected CYGNSS Level 3 soil moisture should not be used as a standalone year-round drought-monitoring product in Guinea savanna agriculture. Its strongest value is as part of environment-aware, bias-corrected, multi-sensor systems that account for vegetation, soil moisture state, precipitation history, land cover, and seasonality. Full article

To address the nonlinear amplification of numerical weather prediction (NWP) errors and the difficult trade-off between coverage and sharpness in short-term offshore wind power forecasting, this paper proposes PRWind, a physics-guided framework for short-term probabilistic forecasting. Built upon a Transformer encoder, the framework integrates horizon-adaptively weighted power-curve, persistence, and wind-speed cubic-law baselines; propagates wind-speed uncertainty into a power distribution through a probabilistic wind-speed distribution with Monte Carlo integration; and employs spatial graph convolution to model inter-turbine correlations, together with online bias correction and segmented post hoc interval calibration. The framework is validated on two offshore wind farms in coastal China (8350 kW and 10,000 kW units), with all data analyzed at hourly resolution and forecasts issued for 23 hourly steps ahead. For point forecasting, the NMAE reaches 8.74% and 8.24%, with MAE reductions of 19.1% (95% CI: 18.1–19.9%) at Wind Farm A and 7.8% (95% CI: 7.3–8.4%) at Wind Farm B relative to the strongest baselines, both statistically significant under the Diebold–Mariano test (p < 0.001). For probabilistic forecasting, the PICP remains stable at 0.89, balancing coverage and sharpness. The error of PRWind accumulates markedly more slowly with the forecast horizon than that of the baselines, and ablation studies confirm the synergistic effect of the modules. Trained and evaluated independently on the two wind farms, PRWind performs consistently, demonstrating cross-wind-farm stability (rather than zero-shot generalization) conferred by physical guidance, and offering significant engineering value for intra-day and day-ahead offshore wind power dispatch. Full article

Cognitive load and foreign-language anxiety are often examined separately in second-language learning, although both may arise when limited processing resources are strained. This meta-analysis examined whether the two constructs covary and why their association matters for models of L2 performance. A PRISMA-guided review identified eleven eligible studies (N = 1250), which were synthesised using a random-effects model. Two independent machine coders repeated the full-text eligibility assessment, effect-size extraction, and quality appraisal; two of the authors then verified every coding against the source reports and resolved all discrepancies. This process recovered one wrongly excluded study and corrected one misextracted coefficient. Greater load was associated with greater anxiety, r = 0.41, 95% CI [0.29, 0.52], and the estimate was r = 0.37, 95% CI [0.27, 0.46], after the most influential study was removed. All included estimates were positive. A specification analysis that substituted every available alternative component, wave, subscale, subgroup, and path yielded pooled estimates from 0.38 to 0.45; setting all eleven studies simultaneously to their least and most favourable alternatives widened the range to 0.29–0.52. Heterogeneity was high, I² = 80.1%, and the 95% prediction interval [0.03, 0.69] extended almost to zero. The pooled estimate therefore represents the centre of a dispersed literature rather than an expected result for a new study. An exploratory contrast between real-time and self-paced tasks was not significant and was confounded with language skill. The review also identified a reporting gap: thirteen additional reports measured both constructs but provided no statistic linking them. Conventional publication-bias diagnostics cannot address this form of selective non-reporting, which means that the pooled estimate is best regarded as an upper bound. This first construct-specific synthesis of the load–anxiety association connects cognitive architecture and attentional control with research on L2 anxiety and indicates why instructional studies should assess cognitive and affective outcomes together. Because the evidence is concurrent, predominantly self-reported, and drawn almost entirely from Chinese-speaking settings, it cannot establish that either construct causes the other or that both reflect a single mechanism. Full article

The psychological processes linking polarization experiences across different online interaction contexts to university students’ value-related judgments and choices warrant further investigation. This study distinguishes experienced polarization within online communities (EPC) from experienced polarization in open online discussions (EPD) and examines their associations with university students’ value-related judgments and choices, including parallel indirect associations through cognitive dissonance response (CDR) and emotional contagion response (ECR). Data from 2105 valid cross-sectional self-report questionnaires collected at a university in China were randomly divided into two subsamples: 770 cases for exploratory factor analysis and 1335 cases for confirmatory factor analysis and structural equation modeling. Indirect effects were estimated using 5000 bootstrap resamples. After adjustment for seven covariates covering demographics, internet use, and online participation, both forms of polarization experience were significantly and positively associated with CDR and ECR. Both psychological responses were also significantly and positively associated with value judgment difficulty (VJD) and value choice susceptibility (VCS). All four direct paths from the two forms of polarization experience to VJD and VCS were significant. Standardized estimates for the eight specific indirect effects ranged from 0.032 to 0.198, and all 95% bias-corrected confidence intervals excluded zero. Together, the predictors in the respective regression equations explained 44.4% of the variance in VJD and 54.5% in VCS. These findings provide statistical support for the hypothesized mediation relationships, indicating a pattern in which direct and indirect associations coexist. By distinguishing polarization experiences in specific interaction contexts, the study jointly connects cognitive and emotional responses to judgment formation and the reference points informing choices. It thereby provides empirical evidence for understanding how university students respond to online opinions and forms the basis for their evaluations and choices. Full article