Search Results (154)

Land-use change affects regional carbon accounting through ecological conversion and the concentration of energy-intensive economic activity. We combined 30 m China Land Cover Dataset maps for 2010, 2015, 2020, and 2025 with provincial socioeconomic and energy statistics to quantify land-use transitions and carbon emissions across nine Yellow River Basin provinces. Construction-land-associated emissions were decomposed using the logarithmic mean Divisia index, factors associated with land expansion were examined using random-forest models, and three 2030 scenarios were evaluated. Construction land expanded by 38.87% from 2010 to 2025, with 71.33% of new construction land converted from cropland and 17.75% from grassland. Net land-use carbon emissions increased by 69.82%, from 1139.06 to 1934.33 million t C. Economic-output density contributed 1144.84 million t C to the increase in construction-land-associated emissions, compared with 576.11 million t C from land expansion, whereas declining energy intensity offset 922.72 million t C. Projected 2030 emissions ranged from 2124.72 million t C under ecological protection to 2866.55 million t C under urban expansion. Construction-land expansion was substantial, but economic-output density made the larger positive contribution to historical emission growth. The projected 2030 estimates depended on the combined trajectories of construction-land demand, economic growth, and energy intensity. These findings highlight the importance of coordinating land-use planning, economic development, and energy-efficiency improvement for sustainable low-carbon transitions. Full article

Recent research has advanced fine-scale mapping and driver analysis of cropland fragmentation, but composite indices may mask structurally different fragmentation configurations, and evidence on how terrain and urban-system location jointly relate to fragmentation remains limited in rapidly urbanizing delta regions. This study quantified cropland fragmentation in the Yangtze River Delta (YRD), China, between 2000 and 2020. A Cropland Fragmentation Index (CFI) integrating edge density (ED), patch density (PD), and mean patch area (MPA) was calculated at a 1 km grid scale, and K-means clustering was used to identify fragmentation configurations. Pearson correlation and random-forest regression were used to examine spatial associations with selected 2020 natural and socioeconomic variables. Cropland area declined by 7.97%, while the regional-mean CFI increased from 0.29 to 0.32. Four configurations were identified, with the largest type (38.45% of grids) characterized by small patches and complex boundaries. Elevation and slope showed the strongest bivariate correlations with CFI, whereas distance to urban areas had the highest random-forest importance. These results reveal distinct fragmentation pathways and support differentiated cropland management in rapidly urbanizing regions. Full article

Windthrow is a major disturbance risk for radiata pine (Pinus radiata D. Don) plantations, but operational susceptibility models must transfer across regions and storm events. We developed a multi-regional framework combining airborne laser scanning (ALS), aerial imagery, mapped stand and site variables, climate, soils, and event-period weather. These data were used to detect storm damage and model windthrow susceptibility following major storm events in Gisborne, Hawke’s Bay and Tasman, New Zealand. Windthrow was mapped from repeat ALS canopy-height differencing in Gisborne and Hawke’s Bay, and from post-storm aerial imagery in Tasman, producing 29,244 balanced windthrow and no-windthrow plot observations. Random-forest models were evaluated using stand-grouped, spatially blocked and leave-one-region-out validation. Stand structure provided the strongest predictive signal, with windthrow concentrated in older, taller and higher-volume stands. Adding long-term climate produced the largest improvement beyond the Base stand/site formulation, giving a pooled ROC–AUC of 0.901 ± 0.013. Spatially blocked ROC–AUC for the selected model ranged across the three regions from 0.780 to 0.856, while leave-one-region-out ROC–AUC ranged from 0.649 to 0.802, demonstrating useful but region-dependent transfer. Adding soil and event-period weather did not consistently improve transferability. Prevalence-calibrated conditional scenario estimates increased with stand development under the mapped regional prevalence and the conditions represented by the reference events. These estimates provide a scalable basis for comparative windthrow-risk screening but should not be interpreted as independently validated absolute or annual windthrow probabilities. Full article

China’s national Emissions Trading System expanded from power to steel, cement and aluminum in March 2025. We examine daily carbon emission allowance price predictability using 1203 trading-day prices. Eleven models and a 26-predictor baseline undergo nested expanding-window validation. Separate common-sample sensitivities add pre-open GFS weather, an official ten-day coal price, a conservatively lagged national generation proxy and official macroeconomic first releases. Across 952 forecasts, the random walk has the lowest RMSE (1.242 CNY/t); the stabilized neural network reaches 1.251. The exact-release/first-public macro specification lowers random-forest RMSE from 1.289 to 1.266, whereas the public energy/weather specification records 1.275; neither beats the benchmark. Technical variables retain the largest model attribution, but even a technical-only forest records 1.253. Ljung–Box and BDS tests detect dependence, while sign runs do not reject sign independence and the variance-ratio null is rejected only at the two-day horizon. Rolling and multiple-break tests find no expansion-date shift. Volatility rankings remain loss-dependent. Statistical dependence therefore exists without stable point-forecast gains. The added energy measures do not represent observed national daily load, and execution returns are not inferred from daily OHLC data. Full article

Warming is making drought and heat co-occur more often, intensifying compound stress on cropland in northern China’s drylands, where water scarcity makes further irrigation expansion untenable. On the Guanzhong Plain, a major grain-producing region, it remains unclear which irrigation conditions buffer cropland carbon–water productivity—the carbon assimilated per unit of water consumed—against such events. Using growing-season (March–October) remote sensing for 2016–2024, we identified the 2022 compound drought–heat event pixel by pixel and classified cropland as stable rainfed, transitional/mixed, or stable high-coverage irrigation from the pre-event (2016–2020) irrigated-area fraction and its persistence. In 2022, 88.9% of cropland experienced at least one compound dry–hot day, with dry–hot overlap 2.4 times the independence expectation. After balancing climate, terrain, soil, spatial position, and subpixel cropland fraction, stable high-coverage irrigation held no advantage in event-year resistance, post-event recovery, or overall resilience. Carbon assimilation remained above its pre-event level while carbon–water productivity fell, so the event cost water-use efficiency rather than carbon. Event-period root-zone soil-moisture change ranked first, and irrigated-area fraction last, among twelve random-forest predictors of resistance. We flagged 6722.3 km² of cropland for water-use audits and 2418.1 km² for supplemental irrigation; limited water should be allocated by realized moisture status, exposure, and resilience, not by irrigability. Full article

Background: Variants of uncertain significance (VUS) in the LDLR gene remain a major barrier to the molecular diagnosis of familial hypercholesterolemia. Although computational approaches offer scalable prioritization, their clinical utility is limited by predictor discordance, incomplete annotations, and inflated performance arising from variant-type imbalance. We aimed to develop a calibrated, uncertainty-aware machine learning framework for LDLR VUS triage. Methods: We developed an end-to-end pipeline integrating multi-source variant annotations and 45 engineered features (expanded to 68 encoded features) to train RandomForest, XGBoost, and support vector machine models. The framework incorporates predictor concordance analysis, splice-aware annotation, and explicit uncertainty modeling. Models were trained using nested cross-validation and evaluated on an independent ClinGen FH-VCEP expert-panel dataset. Results: The XGBoost model achieved high discriminative performance (ROC-AUC up to 0.99 for all variants and ~0.97 for missense variants). Among 1092 LDLR VUS, 69% were assigned a directional classification, including 485 (44.4%) pathogenic-leaning and 268 (24.5%) benign-leaning variants. The remaining 31% were explicitly categorized as predictor-discordant (n = 170, 15.6%) or unresolved (n = 169, 15.5%), preserving uncertainty. Predictor discordance was particularly enriched among missense variants, indicating that naive aggregation of in silico predictors may lead to overclassification. Conclusions: This framework provides a scalable, transparent, and clinically aligned approach for LDLR VUS prioritization, generating ACMG/AMP-compatible computational evidence to support expert curation and downstream functional validation. Full article

Accurate modeling of photovoltaic (PV) cell behavior under varying operational conditions is essential for optimizing energy yield and system reliability. This study presents an extended simulation-based methodology for analyzing monocrystalline silicon PV cells using a double-diode model (DDM) with a physics-based, temperature- and irradiance-dependent parameterization. Building on a SPICE-equivalent circuit formulation, the governing implicit DDM equation is solved numerically to regenerate every current–voltage (I–V) and power–voltage (P–V) curve, and all circuit, block and flow diagrams are redrawn as vector-quality figures. Beyond the deterministic analysis, the manuscript introduces two extensions: (i) a quantitative statistical analysis of the influence of temperature (T), irradiance (G) and series resistance $\left(R_s\right)$ on open-circuit voltage, short-circuit current, maximum power and fill factor, using linear/log-linear regression, a multiple linear regression model and a Pearson correlation analysis; and (ii) a machine-learning (ML) validation study in which a random-forest surrogate model is trained on a 600-point physics-consistent synthetic dataset spanning the full $(T, G, R_s)$ operating envelope and evaluated with a held-out test split and 5-fold cross-validation. The surrogate reproduces the DDM outputs with cross-validated coefficients of determination above 0.98 for maximum power, open-circuit voltage, short-circuit current and fill factor, confirming that the DDM response surface is smooth, learnable and suitable for fast surrogate-based design optimization and maximum-power-point-tracking (MPPT) algorithm testing. Simulated outputs at standard test conditions (25 °C, 1000 W/m², AM 1.5) are compared against manufacturer datasheet values, and residual errors are analyzed and attributed to specific modeling assumptions. Full article

Mineral prospectivity mapping (MPM) increasingly relies on machine learning classifiers, but model performance may be overstated when spatial dependence, extrapolation beyond the training domain, and poorly calibrated prediction scores are not adequately addressed. This study presents a reproducible workflow for regional Au-Ag prospectivity mapping in Nevada that integrates positive–unlabeled learning with Random Forest models and spatial GroupKFold cross-validation. Predictions were restricted to the Area of Applicability (AOA), defined using the 0.95 quantile of a training-derived feature-space dissimilarity index, resulting in areal coverage of 54.331% for Ag and 73.118% for Au. Discrimination based on pooled out-of-fold (OOF) predictions was strong for both commodities (Ag:AP = 0.745, ROC-AUC = 0.908; Au:AP = 0.777, ROC-AUC = 0.912). Post hoc OOF calibration with isotonic regression reduced Brier scores and improved probability reliability within the supported prediction domain. Area-based validation showed that the highest-ranked 10% of the AOA captured 85.6% of the Ag and 84.2% of the Au pooled-OOF positives, corresponding to targeting efficiencies of 8.6-fold and 8.4-fold relative to random spatial selection. Ensemble dispersion was used to map predictive uncertainty and to identify areas where additional data may be most useful for reducing uncertainty. SHAP importance and partial-dependence diagnostics consistently highlighted proximity to Quaternary faults, elevated slip and dilation tendency, and potential-field edges associated with intrusive or volcanic contacts, while surface heat flow acted mainly as a permissive control. The resulting AOA-masked prospectivity maps, uncertainty layers, and top-k targeting products provide a transparent regional framework for prioritizing follow-up exploration and allocating reconnaissance effort. Full article

Reliable estimation of liquid accumulation in gas-well tubing is important for characterizing liquid-loading conditions and supporting engineering assessment. Traditional mechanistic approaches commonly depend on an extensive set of wellbore descriptors and empirical parameters, while also requiring complicated solution procedures. This work addresses these constraints through a data-driven predictive framework that couples ensemble feature selection with ant-colony-optimized support vector regression (ACO-SVR). A majority-voting scheme was applied to 107 production-test records collected from a gas field. The scheme combined linear regression, grey relational analysis, random-forest mean decrease in impurity, the Pearson correlation coefficient, and SHAP attribution, and selected seven dominant factors from 11 candidate variables: casing pressure, tubing pressure, tubing depth, reservoir mid-depth, daily gas production, daily water production, and wellhead temperature. Ant colony optimization subsequently determined the SVR hyperparameters. Evaluation with 32 held-out well samples produced a root-mean-square error of 165.73 m, a mean absolute error of 103.26 m, a coefficient of determination of 0.94, and a mean relative error of 2.11%. Repeated five-fold cross-validation further yielded an average $R^2$ of $0.91\pm 0.04$ and an RMSE of $181.6\pm 24.8$ m, indicating moderate variability across alternative data partitions. Relative to the untuned SVR, ACO-SVR lowered the root-mean-square error and mean absolute error by approximately 27.0% and 31.1%, respectively. Its mean relative error was also 3.66 percentage points below that of the PLATA model. The resulting framework provides accurate prediction of tubing liquid accumulation height from a small sample and offers quantitative information for liquid-loading assessment under the investigated operating conditions. Full article

Landfill leachate circuits that operate without discharge, by recirculating aerated leachate onto the waste mass, are widespread in South-East Europe, yet their long-term behaviour is rarely documented with sample-level data. This study reports a six-year (2020–2025) seasonal monitoring campaign at a sanitary landfill in northern Serbia (alluvial aquifer of the Sava River, transboundary Danube basin) and re-examines it with a transparent multivariate protocol. Seventy-two leachate samples (collection well, aeration lagoon, sedimentation lagoon; n = 24 each, 30 parameters), 28 realised surface-water campaigns, and six years of groundwater summaries were evaluated by principal component analysis/factor analysis (PCA/FA, Varimax normalized), hierarchical cluster analysis, PERMANOVA, non-parametric paired tests and, for benchmarking, supervised machine learning. The pooled leachate model (n = 72; 21 variables; KMO = 0.700; Bartlett χ² = 956, p < 0.001) retained four factors by parallel analysis, explaining 61.6% of total variance; after rotation the factors accounted for 27.7%, 14.3%, 10.4%, and 9.3%. Factor 1 grouped organic load with particle-reactive metals (COD, BOD₅, Fe, Ni, Cr, As, Zn), Factor 2 a reduced sulfur–fluoride–BTEX signature, Factor 3 temperature-driven nitritation, and Factor 4 a nitrate–manganese redox contrast. Crucially, paired campaign-by-campaign comparison showed no removal of the dominant pollutants along the circuit. Median COD, BOD₅ and NH₄-N were not lower in the sedimentation lagoon than in the collection well, while pH rose from 8.08 to 8.75 (p < 0.001); only Cu, Pb, NO₃-N, and NO₂-N decreased significantly. The circuit therefore homogenises and concentrates dissolved load rather than removing it. Downstream surface water was significantly enriched in electrical conductivity (+110 µS/cm), total dissolved solids, NH₄-N, and NO₂-N relative to upstream (Wilcoxon, p < 0.05), and groundwater showed episodic conductivity up to 12,760 µS/cm and NH₄-N up to 102 mg/L. Cytotoxicity (MTT) confirmed biological relevance, with MRC-5 viability falling to 37% after 24 h exposure to 50 vol.% groundwater (Pw3) versus 60% in A549 cells. A random-forest classifier separated circuit units far better than PCA-based discrimination (76.4% versus 54.2% cross-validated accuracy) and distinguished the 2020–2021 pandemic period from 2022–2025 with 94.2% accuracy, a period effect also confirmed by PERMANOVA (R² = 7.2%, p < 0.001). The results indicate that closed-loop recirculation without an engineered discharge barrier transfers, rather than eliminates, contaminant load, and that after-care of such systems requires mass-balance monitoring and polishing treatment. Full article

Magnetic shape memory alloys based on the Ni–Mn–Ga system are of strategic interest for aerospace and robotics applications due to their ability to respond to both thermal and magnetic stimuli. However, the NASA Shape Memory Materials Database a key resource for the community exhibits significant gaps in functional parameters, with up to 93.7% of records missing critical properties such as the Curie temperature, and over 88% lacking complete magnetic data. To address this limitation, this study proposes a data imputation strategy based on a stacking ensemble comprising twelve machine learning models (LGBM, XGBoost, CatBoost, GradientBoosting, RandomForest, MLP, BayesianRidge, KNN, SVR, GPR, MICE, and AutoEncoder), optimized via Optuna and evaluated using ten random seeds with 10 repetitions each. The approach was applied to reconstruct missing entries in NASA’s database. For heat treatment 1, the method achieved coefficients of determination ($R^2$) of 0.95 for duration (h) and 0.88 for temperature (°C), respectively. For the phase transformation temperatures (Mf, Ms, As, and Af), the method yielded $R^2$ values of 0.83, 0.82, 0.79, and 0.80, respectively. Magnetic properties saturation magnetization and maximum magnetic field were imputed with an $R^2$ of 0.92. In contrast, the Curie temperature exhibited limited predictive performance ($R^2$ = 0.15–0.35), primarily due to insufficient data availability. Overall, the proposed methodology integrates machine learning based imputation with physically supported constraints, providing a viable alternative to enhance the completeness and utility of materials databases. Full article

Within a single flood event, this study evaluated the empirical relationship between underwater hyperspectral observations and layer-scale suspended sediment concentration (SSC) and calibrated its predictive uncertainty. The dataset comprised 101 paired observations from 25 complete four-position verticals (n = 100) plus one isolated record. Sample-level leave-one-out cross-validation (LOOCV) provided the primary estimate of event-internal layer-level interpolation, while leave-one-vertical-out validation (LOVO) assessed sensitivity to an unseen vertical. Under LOOCV, random-forest models reached R² ≈ 0.78–0.79 and RMSE ≈ 0.087 kg/m³; on the complete-vertical subset, spectra-only RF_log1p decreased to R² = 0.595 and RMSE = 0.120 kg/m³ under LOVO. Discharge and interpolated stage added event-specific context (Q/stage-only LOVO R² = 0.699), but cross-date transfer remained poor. Raw quantile-regression-forest intervals under-covered the nominal 90% level; conformal calibration restored coverage at the cost of wider intervals (grouped PICP90 0.721 → 0.927; MPIW90 0.234 → 0.617 kg/m³). The four positions showed a weak within-vertical effect (Friedman χ² = 11.93, p = 0.0076; Kendall’s W = 0.159), with no pairwise contrast significant after Holm correction. The results support calibrated event-internal interpolation at sampled layers, but do not resolve a monotonic vertical profile or support transfer across dates, stages, stations, or events without recalibration. Full article

The rapid dissemination of misinformation through online social networks creates challenges for information reliability and network stability. We evaluate a multimodal framework that produces a credibility classification and a propagation-level prediction within a single processing pipeline. The framework combines frozen RoBERTa features, engineered interaction statistics, a standard GCN, and Transformer self-attention over modality representations. These are established components; the purpose of the framework is to integrate the available modalities and return both outputs from one model, rather than to introduce a new encoder or attention mechanism. On 12,701 MCFEND news items, the framework obtains 94.77% accuracy and 95.79% F1-score for credibility classification, together with an MAE of 0.34, RMSE of 0.44, and R² of 0.96 for propagation-level prediction. The small variation across five matched seeds supports the stability of these means under the fixed protocol. The credibility F1 difference from modality-matched MLP-Fusion is not significant after Holm correction (adjusted p = 0.0810), and five pairs provide limited power for detecting small differences; the two implementations are therefore interpreted as having close performance. RandomForest also obtains lower propagation errors than the evaluated framework. The implemented retrospective fractional-observation protocol observes a fraction defined by final cascade size and uses snapshot engagement values with a fixed random split. In one diagnostic, replacing the fractional-observation rule with fixed K = 15 preserves classification F1 at a similar level but reduces the framework’s propagation R² from 0.963 to 0.896. In a separate target-definition diagnostic conducted with the original observation setting, predicting residual future interactions yields an R² of 0.870. The findings therefore characterize retrospective within-dataset prediction and do not establish leakage-free early forecasting. The two outputs are interpreted separately because the present experiments evaluated one shared dual-output configuration rather than comparing it with two independently optimized systems. Full article

This study proposes a quality-aware multimodal framework for health-state assessment and remaining useful life (RUL) prediction of cold-region tunnels. The framework integrates structural-response, environmental, apparent-defect, and engineering-inspectiondata, with the apparent-defect pathway jointly encoding raw images through a convolutional neural network and structured defect variables. Five data-quality dimensions-completeness, accuracy, consistency, timeliness, and traceability are incorporated intoreliability-guided multimodal fusion. Their base weights were re-audited through two rounds of expert consultation, each comprising 323 valid questionnaires. The Cr-weighted group analytic hierarchy process yielded weights of 0.0548, 0.1326, 0.1372, 0.2279, and 0.4474, respectively, with a group consistency ratio of 0.0455; the ranking remained stable under one-at-a-time +10% perturbations. In the primary tunnel case study, the framework achieved 89.7% health-state accuracy, a 6.3% RUL mean absolute percentage error, and 84.1% accuracy under Gaussian perturbation of standardized numerical inputs at a noise scale of 0.15. To further examine the reliability contribution of data-quality information, an independent field panel comprising 600 segment-month observations from 25 segments across three operational tunnels was evaluated using target-excluded specifications, two-way fixed effects, leave-one-tunnel-out validation, multiple baseline models, and five fixed random seeds. A one-standard-deviation increase in lagged quality instability was associated with a 0.0151 increase in the subsequent state-error index (95% CI: 0.0118-0.0184; p < 0.001). In cross-tunnel random-forest tests, incorporating quality information increased mean R² from 0.8277 to 0.8323 for state-error prediction and from 0.8517 to 0.8673 for RUL-contraction prediction, with both improvements significant in paired tests (p < 0.001). Split-conformal intervals achieved mean cross-tunnel coverage of 95.8% and 95.9%, respectively. These findings demonstrate that data-quality information provides a modest but statistically supported improvement in cross-tunnel reliability, whilethe principal contribution lies in integrating auditable data governance, reliability-aware fusion, and engineering decision support within a unified tunnel health-management framework. Full article

Accurate prediction of input–output relationships in natural gas exploration is essential for improving exploration efficiency and optimizing investment allocation. However, this task is severely hindered by data sparsity and strong nonlinear characteristics inherent in oil and gas exploration systems, rendering conventional statistical methods and single machine learning models ineffective. This study develops a novel integrated framework combining data augmentation, nonlinear feature engineering, and ensemble learning to achieve accurate and interpretable prediction of exploration input–output matching under limited data constraints. Taking four core exploration indicators—including the number of exploration wells, total drilling depth, reserve abundance, and proven reserves—as input variables, adaptive prediction models were constructed for seven typical hydrocarbon basin exploration systems. To ensure comprehensive algorithmic exploration, nine advanced algorithms, including mainstream ensemble methods (RandomForest), few-shot neural networks (FewShot_NN), and kernel-based regressions, were systematically benchmarked. Furthermore, SHapley Additive exPlanations (SHAPs) was adopted to enhance model interpretability, and non-parametric Wilcoxon signed-rank tests were introduced to rigorously validate statistical significance. The results demonstrate that the optimal predictive pathway varies across different geological systems. Specifically, RandomForest and GBDT exhibit superior performance in systems with moderate heterogeneity (e.g., Jialingjiang and Changxing–Feixianguan Formations), whereas FewShot_NN and Kernel Ridge achieve the highest accuracy under extreme data sparsity and volatility (e.g., Xujiahe Formation and Lower Permian). The established framework yields a coefficient of determination (${\mathrm{R}}^2$) greater than 0.96 for the majority of study cases, with overall absolute percentage errors heavily minimized. SHAP analysis further verifies that drilling depth and reserve abundance are the dominant controlling factors. This data-driven framework provides a robust and interpretable technical tool for the intelligent management of energy resources. Full article