Search Results (125)

Surrogate-assisted optimization can reduce the number of repeated solid rocket motor (SRM) simulations, but a globally accurate surrogate is not necessarily reliable where design decisions are made: near active constraints. This study develops a low/medium-fidelity surrogate workflow for SRM performance prediction and constraint-robust screening. The low-fidelity response is obtained from a zero-dimensional internal-ballistics solver, and the medium-fidelity response is obtained from SrmStudio, an engineering-level SRM internal-ballistics design and simulation software. Residual correction and autoregressive Co-Kriging (AR1) models are assessed through global holdout accuracy, boundary-focused subsets, calibrated predictive intervals, same-budget enrichment comparisons, and SrmStudio consistency replay. Although the global coefficient of determination exceeds 0.99, the burn-time-boundary and pressure-exponent-sensitive subset MAPEs are approximately 28–47% higher than the final five-response global mean; the separation-boundary and high-pressure subsets do not exhibit the same increase. Calibrated 90% AR1 intervals achieve 90.8–96.7% coverage on the boundary test set and identify regions in which mean-only feasibility decisions are less reliable. A direct weight search further shows how sampling performance changes with the score composition after a boundary pool has been identified. Tolerance-boundary augmentation reduces the local thrust-window prediction error from 0.802 to 0.062 percentage points. Within the adopted simulation hierarchy, SrmStudio replay identifies a numerically promising candidate whose mean thrust increases from the SrmStudio baseline of 3866.67 N to 4000.67 N, a simulator-relative improvement of 3.47%, while satisfying the nominal constraints. Robustness analysis shows that the pressure exponent n is the limiting uncertainty, with an admissible calibration boundary of only ±0.191%. Full article

Uncertainty quantification of well-log interpretation is essential to derisking subsurface exploration and development decision-making by providing possible scenarios for reservoir property distribution, fluid flow behaviors, and hydrocarbon potential. Well-log interpretation offers crucial insights into permeability variations, reservoir compartmentalization, mineral composition, and fluid mobility. Inherent uncertainties, such as those arising from geological heterogeneity, limited sampling, and non-uniform distribution of rock properties, can lead to inaccuracies that compromise petrophysical interpretation and formation evaluation. However, traditional data-driven well-log interpretation methods, which map well logs to formation properties based on core measurements, are primarily deterministic and fail to quantify uncertainty accurately. By leveraging deep learning and generative models, we introduce a probabilistic approach that significantly improves permeability estimation and uncertainty quantification. Our methodology integrates co-kriging techniques with Conditional Generative Adversarial Networks (cGANs) and Conditional Variational Autoencoders (cVAEs), establishing a quantitative relationship between kriged core, well-log data and permeability. Our approach enhances petrophysical property uncertainty estimations based on geostatistics by establishing a quantitative relationship between kriged estimates and flow-related properties. Training features are constructed using collocated co-kriging, capturing the cross-correlation between well logs (input features) and core data (output formation properties). Core bulk density, calculated from grain density, is kriged to well-log resolution to enable porosity estimation, while permeability is similarly kriged. A low-pass filter is then applied to smooth the kriged core bulk density, permeability, and estimated porosity, ensuring more accurate interpretations. The results reveal that cGANs and cVAEs consistently produce lower uncertainty estimates compared to traditional machine learning models. High-permeability zones exhibit lower uncertainty (approximately 3–5%), while low-permeability zones show higher uncertainty (10–15%). Traditional deep learning models tend to overestimate uncertainty, whereas generative models provide more reliable estimates. Additionally, applying kriged permeability data improves uncertainty estimations, further reducing uncertainty to 3% in high-permeability zones and 10% in low-permeability zones. To ensure broad applicability, the methods were tested on datasets from both carbonate and clastic reservoirs. In carbonate formations, prior classification steps are necessary to achieve accurate permeability predictions. The interpretation workflow improves permeability estimation accuracy and enhances uncertainty quantification across conventional and unconventional reservoirs. Additionally, this method is adaptable for CO₂ injection and H₂ storage wells, demonstrating versatility across various reservoir types. Full article

Reliable quantification of crop evapotranspiration (ET_c) at field resolution is a prerequisite for evidence-based irrigation scheduling in agricultural systems subject to nitrate leaching constraints. This study presents and evaluates a multi-sensor data fusion framework integrating MODIS Terra (500 m, daily) and Sentinel-2 (10–20 m, 5-day revisit) imagery to generate cloud-robust, daily ET_c maps for an 87.5 ha irrigated maize field in Nyírbátor, Hungary, during the 2020 and 2021 growing seasons. Three gap-filling strategies for missing Sentinel-2 NDVI observations were systematically compared: (i) co-regionalisation with cokriging, (ii) local time series interpolation of MODIS pixel centres using ordinary kriging, and (iii) a median time series of cotemporal MODIS pixels—a novel approach developed to suppress sub-pixel spectral contamination from roads and irrigation infrastructure. For field-mean temporal reconstruction, the median approach consistently outperformed the alternatives (adjusted R² = 0.81, NRMSE = 0.15–0.17; pixel-wise correlation 0.70–0.85), effectively filtering heterogeneous landscape artefacts. Daily crop coefficients (K_c) derived from fused NDVI time series via the FAO-56 framework yielded ET_c ranging from 0.99 mm day⁻¹ (initial stage) to 6.40 mm day⁻¹ (peak crop development). Seasonal precipitation–ET_c deficit analyses revealed contrasting patterns: near balance in 2020 versus an 85 mm mid-season deficit at critical nodes in 2021, demonstrating the potential utility of spatially explicit daily ET_c monitoring for irrigation scheduling. These deficit estimates represent irrigation demand indicators; a complete water balance would additionally require measured irrigation volumes, soil water storage changes, deep percolation, and surface runoff data. The methodology provides a proof-of-concept framework for EU Nitrates Directive compliance monitoring, relying solely on freely available satellite data. Independent ET_c validation is required before operational deployment, and transferability to other crops and regions requires validation across contrasting pedoclimatic conditions. Full article

The Chihuahuan Desert Biome (CDB), the largest semi-arid region in North America, has undergone repeated climatic fluctuations during the Interglacial–Glacial Oscillation (IGO) of the last eight million years. We investigated biogeographic and evolutionary patterns of endemic cacti within the present-day Interglacial and the Last Glacial by examining 119 strict endemics, including 75 suitable for Species Distribution Modeling (SDM) and 44 microareal strict endemics, together representing 36.17% of the 329 species in the biome. Cacti probably originated in South America after substantial separation from Africa, with pollen fossils documenting their presence in Mexico by 51.6 Ma. Climatic reconstructions for each phase were developed using regional numerical and co-kriging methods following Sánchez-Santillán and García, complemented by paleoclimatic evidence from Scotese, Roy-Priyadarsi, Van Devender, and Betancourt. MAXENT SDMs and PANBIOTRACKS’ track-node analyses were applied to 3719 specimens representing 2015 localities to explore the colonization patterns and broad evolutionary trends. Combined suitability layers and panbiogeographic analyses revealed a predominant southeastern-to-northwestern colonization pattern, largely following the western flank of the Sierra Madre Oriental and intermontane valleys. The northern sectors were less diverse, more arid, and apparently colonized more recently, whereas the southern sectors concentrated much of the endemic richness and connectivity. The concordance among climatic suitability patterns, tracks, nodes, and the available phylogenetic evidence supports a major role of climatic oscillations in shaping the spatial and evolutionary history of endemic cacti throughout the CDB. Full article

Traditional meteorological data spatial interpolation methods often rely on linear or static assumptions, which are inadequate for complex terrain and fail to exploit continuous spatiotemporal variation information. This paper proposes a Spatiotemporal Graph Network with Adaptive Temporal Decay (DG) that integrates a learnable graph convolution module and a temporal attenuation mechanism, enabling accurate precipitation estimation for target stations or regions at consecutive time steps. The method is evaluated using daily precipitation data from nine stations in Longnan City, Gansu Province, China, along with ERA5 (0.25°) and GPCP (0.5°) gridded reanalysis products. In the station-to-station interpolation scenario, DG significantly outperforms ordinary Kriging (OK), reducing the average RMSE from 1.4 mm/day to 1.2 mm/day, with a 28.6% improvement at mountainous stations. The DG model also exhibits superior performance in grid-to-station interpolation, achieving an average RMSE of 1.9 mm/day (OK: 2.5 mm/day). On heavy precipitation days (≥20 mm/day), DG reduces the RMSE nearly by half (11.7 mm/day) compared to OK (23.2 mm/day). A temporal-only LSTM baseline and three ablation variants (spatial-only OSI, temporal-only OTI and dgcn-only OD) are also compared, and DG consistently outperforms them, confirming the essential role of spatiotemporal integration. Additional baselines including IDW and Co-Kriging further validate the superiority of DG. The proposed method offers a promising new approach for high-precision spatiotemporal interpolation of meteorological elements in complex terrain. Full article

High-resolution spatiotemporal sea ice concentration (SIC) estimates are essential for Arctic navigation and ice analysis, but existing observational products are often too coarse, and physics-based models are computationally expensive. This study proposes a data-driven framework that couples Light Gradient Boosting Machine (LightGBM) temporal prediction with Kriging-based spatial interpolation to reconstruct SIC fields over the Northern Sea Route sector. LightGBM is trained on a grid-based SIC time series with engineered features representing persistence, seasonality, and short-term variability, enabling multi-horizon forecasting across large spatial grids. The predicted SIC fields are then refined using Ordinary Kriging (OK) and Co-Kriging (CK) with Gaussian and spherical semi-variogram models. Prediction performance is evaluated using root mean square error, and interpolation accuracy is assessed through cross-validation. Results show that, for high-latitude regions and resolutions finer than 0.25° × 0.25°, OK with a spherical semi-variogram achieves lower interpolation errors than CK and Gaussian-based alternatives. By sequentially coupling temporal learning and spatial refinement, the proposed framework improves temporal continuity, spatial structure, and error quantification, providing high-resolution SIC information suitable for large-scale Arctic ice analysis and navigation support. Full article

Spiral-groove dry gas seals are widely used in various rotating machinery, and their performance prediction is of great significance for structural design and operational optimization. Existing studies still face several limitations, including the limited fidelity of numerical simulations, the insufficient number of experimental samples, and the restricted generalization capability of models based on a single data source. To address these issues, this study constructed a multi-source data system integrating numerical simulation data and experimental data, and systematically compared four representative data fusion methods, namely the uncertainty-weighted fusion algorithm, TrAdaBoost, MFDNN, and CoKriging, with analysis of their applicability and predictive performance. The results show that multi-source data fusion can effectively exploit the complementary advantages of different data sources and improve the prediction accuracy of dry gas seal performance. In terms of the comparison of data fusion methods, all four methods achieved good results for the groove-depth problem; however, for the spiral-angle and groove-number problems, which exhibit stronger nonlinear characteristics, clear differences were observed among the methods. Among them, TrAdaBoost showed the best overall performance, followed by MFDNN, then CoKriging, while the uncertainty-weighted method was relatively weaker. In terms of seal performance, the influence of groove depth on seal performance was relatively direct; the spiral angle is recommended to be controlled within 10–14°, and the groove number within 12–16, so as to balance opening force and leakage rate. This study can provide a reference for the rapid performance prediction and parameter optimization of spiral-groove dry gas seals. Full article

This study explored the application of carbon stable isotopes for tracing the geographical origin of Monochamus alternatus, an insect vector responsible for spreading pine wilt disease. The primary vector of pine wilt disease, an aggressive disease caused by the pine wood nematode and affecting pine forests, is Monochamus alternatus. Samples of Monochamus alternatus were collected from 12 provinces across China, and their carbon isotope ratios (δ¹³C) were measured. By analyzing the correlation between these ratios and various environmental factors, including latitude, longitude, altitude, and bioclimatic conditions, it was found that precipitation seasonality and solar radiation were the most important factors influencing the carbon isotope ratio of Monochamus alternatus. The spatial distribution of Monochamus alternatus carbon isotopes in China was predicted using the co-Kriging interpolation method, incorporating these two environmental variables. The findings revealed a gradient in the carbon isotope ratio of Monochamus alternatus, which could help differentiate the species across various geographical regions in China. Full article

Effective management of wastewater discharges requires understanding the spatial distribution of pollutants both within engineered infrastructure and in receiving environments. However, spatial data sparsity constrains comprehensive assessment. This critical review examines the role of Geographic Information Systems (GIS) and statistical interpolation techniques in bridging these data gaps for wastewater-derived pollutants. Moving beyond a simple compilation of methods, this paper provides a synthesizing framework that categorizes and evaluates interpolation techniques-from deterministic and geostatistical approaches to emerging machine learning (ML) and hybrid models- based on their ability to address specific challenges in wastewater systems. A key contribution is a systematic review and meta-analysis following PRISMA guidelines, synthesizing evidence from 22 studies that directly compare interpolation methods for wastewater-relevant parameters (BOD₅, COD, nutrients, heavy metals) in both engineered systems and impacted water bodies. Results indicate that machine learning methods significantly outperform traditional approaches, with a pooled 21% reduction in RMSE compared to Ordinary Kriging (95% CI: 15–27%). However, subgroup analyses reveal context dependency: ML advantages are most pronounced for organic pollutants (29% reduction) and data-rich environments (27% reduction with n > 100), while geostatistical methods remain competitive for physical parameters (8% reduction, non-significant) and data-sparse scenarios (12% reduction with n < 50). Co-Kriging achieves 15% RMSE reduction over Ordinary Kriging when auxiliary variables are available. The review explores applications in pollutant tracking, infrastructure planning, and environmental impact assessment, highlighting how integration of real-time sensor data (IoT) and remote sensing is transforming static maps into dynamic monitoring tools. Finally, a forward-looking research roadmap is presented, emphasizing hybrid modeling frameworks, digital twin integration, and improved uncertainty communication for decision support. By quantitatively synthesizing the current state-of-the-art and identifying critical knowledge gaps, this review aims to guide future research towards more intelligent, adaptive, and reliable spatial assessments of wastewater-derived pollutants. Full article

Ground-level ozone pollution poses significant risks to public health and ecosystems and remains a major environmental challenge worldwide. Accurate forecasting is difficult due to the nonlinear formation mechanisms of ozone and its strong dependence on meteorological conditions. This study proposes a Wind Speed and Direction-Based Dynamic Spatiotemporal Graph Attention Network (WSDST-GAT) for multi-step hourly ground-level ozone prediction. The model integrates a wind-aware dynamic graph to represent anisotropic pollutant transport and a Transformer-based temporal encoder to capture long-range dependencies. Meteorological variables are incorporated to enhance physical interpretability and predictive robustness. A co-kriging module is further employed to reconstruct continuous spatial ozone fields with quantified uncertainty. Using hourly observations from 35 monitoring stations in Beijing, WSDST-GAT achieves a Coefficient of Determination of 0.957, with a Mean Absolute Error of 5.25 μg/m³, and a Root Mean Square Error of 9.58 μg/m³. The prediction intervals demonstrate strong reliability with a Prediction Interval Coverage Probability of 94.01% and a Prediction Interval Normalized Average Width of 0.174. These results indicate that the proposed framework provides an accurate and physically informed solution for ozone forecasting and air quality management. Full article

Understanding the spatiotemporal dynamics of medicinal plant distributions and their quality responses under climate change is essential for formulating forward-looking conservation and utilization strategies. In response to the increasing depletion of wild resources of Alpinia officinarum Hance, one of the ‘Ten Major Guangdong Medicinal Materials’, this study developed an integrated modeling platform incorporating nine algorithms. These included generalized linear models, machine learning techniques, and a MaxEnt model optimized using ENMeval (Regularization Multiplier (RM) = 3, Feature Class (FC) = LQH). The platform was applied to simulate habitat suitability evolution under current climatic conditions (1970–2000) and for two future periods (2050s: 2041–2060; 2090s: 2081–2100) across four Shared Socioeconomic Pathways (SSP126, SSP245, SSP370, and SSP585). Furthermore, Co-kriging interpolation was coupled to conduct a comprehensive quality zoning based on the dual “ecological-chemical” dimension. Analysis of key environmental factors revealed that the distribution of A. officinarum is primarily constrained by hydrothermal conditions, with a suitable annual temperature ranges from 19.96 to 29.05 °C and a dry-season precipitation requirement between 56.64 and 185.65 mm. Model projections indicate that future warming does not promote habitat expansion; instead, it drives a latitudinal shift in the suitability centroid toward lower latitudes. The cumulative effects of different emission pathways vary markedly: the high-emission scenario (SSP585) triggers severe habitat contraction by the 2090s, while habitat loss under the SSP370 scenario remains relatively manageable. By overlaying the spatially heterogeneous distribution of galangin, this study delineated southeastern Yunnan, southeastern Guangxi, southwestern Guangdong, and northern Hainan as core “integrated quality regions”. These findings not only reveal the sensitivity and vulnerability of A. officinarum Hance to climate change but also provide spatially explicit guidance for in situ germplasm conservation and the selection of high-quality cultivation bases. Full article

This study aimed to evaluate the effectiveness of spectral absorption-feature indices, derived from soil hyperspectral diffuse reflectance spectroscopy, as covariates within a multivariate geostatistical framework to enhance the digital mapping of soil organic carbon (SOC). The approach also incorporated exhaustively measured auxiliary variables derived from topographic and textural attributes. The research was conducted in a 1.39-km² forested catchment, where 135 topsoil samples (0–0.20 m depth) were collected from soils classified as Typic Xerumbrepts and Ultic Haploxeralfs. All samples were analyzed for SOC concentration, soil texture, and diffuse reflectance spectra across the VIS–NIR–SWIR region (350–2500 nm). The continuum-removal technique was applied to compute radiometric indices associated with absorption features in the visible region and at 1400, 1900, and 2200 nm. Results demonstrated that these indices effectively captured the SOC spatial variability when combined with silt fraction and topographic attributes, which, among the other covariates, actually exhibited the strongest spatial relationships with SOC. Compared to univariate ordinary kriging, the multivariate geostatistical approach yielded improved prediction accuracy in cross-validation, mostly due to the use of hyperspectral indices as auxiliary variables. Moreover, the geostatistical analysis revealed that the multivariate frame of spatial association was characterized by two distinct spatial scales. The findings of this work then support the use of hyperspectral indices as valuable covariates for digital modelling of SOC distribution even in landscapes characterized by heterogeneous topography and pedology. Full article

The integration of active learning methods and multi-fidelity (MF) Kriging models has been demonstrated to effectively reduce the number of high-fidelity (HF) samples required for reliability analysis. However, there is a lack of comparative research on different MF Kriging models in this context. In this work, the performances of the hierarchical Kriging model, dis-Kriging model, and Co-Kriging model are compared within one-stage and two-stage active learning frameworks through five numerical examples. The total cost, the number of HF samples required, and the stability of estimated failure probability are utilized to facilitate a comparative analysis of different MF Kriging models. The results from the five examples indicate the following: the hierarchical Kriging model demonstrates a satisfactory performance in both one-stage and two-stage frameworks; the dis-Kriging model is not adequately equipped to handle complex, highly nonlinear problems; and the Co-Kriging model exhibits a lack of robustness, though it performs well in certain cases. Full article

Temperature and humidity calibration chambers, which provide controlled environments for instrument testing and validation, are widely applied in the aerospace and biomedicine fields. However, traditional fixed calibration points fail to adapt to complex operational requirements and exhibit problems including a limited coverage range and low efficiency. To address these challenges, this study develops a Gaussian Process-based Multi-Fidelity Bayesian Optimization (GP-MFBO) framework for optimal selection of temperature and humidity calibration points. The framework integrates the following three key components: (1) a three-layer progressive multi-fidelity modeling system comprising physical analytical models, computational fluid dynamics (CFD) numerical simulations, and experimental verification; (2) a systematic uncertainty quantification system covering model uncertainty, parameter uncertainty, and observation uncertainty; and (3) an adaptive acquisition function that balances uncertainty penalty mechanisms and multi-fidelity information gain evaluation. The experimental results demonstrate that the proposed GP-MFBO method achieves optimal calibration point combinations with a temperature uniformity score of 0.149 and humidity uniformity score of 2.38, approaching theoretical optimal solutions within 4.5% and 3.6%, respectively. Compared to standard Gaussian process, Co-Kriging, two-stage optimization, polynomial regression, and traditional single-fidelity methods, GP-MFBO achieves uniformity score improvements of up to 81.7% and 76.3% for temperature and humidity, respectively. The prediction confidence interval coverage reaches 94.2%, outperforming all comparative methods. This research provides a rigorous theoretical foundation and technical solution for the scientific design and reliable operation of large-space temperature and humidity calibration systems. Full article

Thailand’s mango export industry faces significant challenges in meeting stringent international quality standards, particularly the costly phytosanitary X-ray irradiation process. Current fixed-dose irradiation methods result in substantial energy waste due to variations in fruit size. This research presents a low-cost, real-time system that integrates computer vision and artificial intelligence (AI) to optimize this process. By capturing a single top-view 2D image, the system accurately estimates the three-dimensional characteristics (width, height, and depth) of ‘Nam Dok Mai Si Thong’ mangoes. This dimensional data is crucial for dynamically adjusting the radiation dose for each fruit, leading to significant reductions in energy consumption and operational costs. Our novel approach utilizes a Linear Regression combined with Co-Kriging (LR + CoK) model to precisely estimate fruit depth from 2D data, a key limitation in previous studies. The system demonstrated high efficacy, achieving a dimensional estimation error (RMSE) of less than 0.46 cm and a size grading accuracy of up to 93.33 percent. This technology not only enhances sorting and grading efficiency but also offers a practical solution to lower the economic and energy burden of phytosanitary treatments, directly improving the sustainability of fruit export operations. Full article