Search Results (3,739)

Soil particle size fractions (PSFs)—sand, silt, and clay—are fundamental determinants of soil hydrological behavior, nutrient retention, and erodibility, yet their spatial prediction remains challenging due to the compositional nature of the data, unquantified prediction uncertainty, and limited interpretability of machine learning models. This study develops an integrated compositional mapping framework incorporating multi-source Sentinel-1/2 and topographic covariates, coupling the isometric log-ratio (ILR) transformation with Quantile Regression Forests (QRFs), a Monte Carlo simulation (MCS)-based latent-to-physical space uncertainty propagation strategy, and a Wrapper-SHAP attribution method to jointly address these challenges. The framework was evaluated across regional croplands in the central Shandong mountain-hilly region of China, using an elevation-stratified spatial cross-validation. Validations achieved R² values of 0.72, 0.61, and 0.59 for sand, silt, and clay, respectively, and a global Aitchison distance of 0.34. Critically, the MCS error propagation strategy effectively compensated for the probability distribution shift introduced by non-linear ILR back-transformation. This ensured that all predicted compositions strictly satisfied compositional closure and the [0, 100%] constraint, while aligning the prediction interval coverage probability (PICP) of each fraction closely with the 90% nominal level. Wrapper-SHAP overcame direct attribution limitations in compositional models, revealing the predictive associations of these multi-source covariates: high remote sensing-derived Bare Soil Index (BSI) and Moisture Stress Index (MSI) values primarily exhibited strong predictive associations with sand enrichment, whereas their lower values, combined with elevated Normalized Difference Moisture Index (NDMI), Enhanced Vegetation Index (EVI), and anthropogenic indicators, favored silt and clay accumulation. The proposed framework provides a transferable methodological reference for remote sensing-integrated compositional soil mapping with reliable uncertainty estimates and interpretable driver identification at regional scales. Full article

Land Surface Temperature (LST) is essential for climate monitoring, drought assessment, and urban heat analysis. Despite its importance, the Landsat-8 Collection 2 Level-2 (C2L2) LST product has not been rigorously validated using ground measurements—a critical gap this study addresses. We present the first comprehensive accuracy assessment using 382 coincident satellite–ground observations collected from seven Surface Radiation Budget Network (SURFRAD) stations distributed across diverse climatic regions of the United States during the period 2023–2025. The validation results indicate strong overall agreement between satellite-derived and ground-measured temperatures, yielding an RMSE of 4.20 °C, a coefficient of determination (R²) of 0.91, and a Pearson correlation coefficient (r) of 0.98. These statistics demonstrate the high reliability of the C2L2 LST product across a wide range of environmental conditions. Nevertheless, a systematic warm bias of 1.75 °C was observed, indicating a tendency toward temperature overestimation. Model performance exhibited pronounced seasonal variability. The highest accuracy was achieved during winter conditions (RMSE = 2.17 °C; r = 0.99), whereas performance declined considerably during summer months (RMSE = 5.84 °C; r = 0.91). Analysis of atmospheric water vapor content revealed significant associations with retrieval errors at high-elevation and arid locations, particularly at FPK (r = 0.78) and DRA (r = 0.75), based on 106 matched observations. These relationships provide important insight into the atmospheric factors contributing to seasonal variations in retrieval accuracy. Temperature-dependent analyses further demonstrated that retrieval uncertainty increases with surface temperature. Performance progressively deteriorated from cooler to warmer thermal regimes, with RMSE values increasing from approximately 2.05 °C for temperatures below 20 °C to 5.71 °C for temperatures exceeding 40 °C. Spatial evaluation also revealed substantial differences among stations. Relatively homogeneous, low-elevation sites exhibited superior performance (GWN: RMSE = 2.60 °C; SXF: RMSE = 2.55 °C), whereas stations located in mountainous or topographically complex environments showed reduced accuracy (TBL: RMSE = 5.14 °C; FPK: RMSE = 5.62 °C). These outcomes emphasize the influence of terrain complexity and atmospheric heterogeneity on LST retrieval performance. Overall, this study establishes the first comprehensive benchmark for evaluating the reliability of Landsat-8 C2L2 LST products. The results provide valuable guidance for their application in climate research, precision agriculture, hydrological modeling, and environmental monitoring. Furthermore, the findings identify specific environmental conditions requiring enhanced validation efforts and suggest opportunities for future algorithm refinement through improved atmospheric correction procedures and more accurate surface emissivity characterization. Full article

Photovoltaic power forecasting is important because solar generation varies with weather conditions. Accurate forecasts help improve grid operation, reduce costs, enhance system stability, and support battery management. This paper presents a hybrid methodology that combines statistical analysis and machine learning to forecast photovoltaic power generation. First, the data are cleaned and preprocessed. Then, the input vector is selected using two criteria: collinearity analysis to remove redundant variables, and Granger causality to identify variables with predictive value in a nonlinear autoregressive with exogenous inputs artificial neural network (NARX-ANN) framework. Next, an experimental design is used to evaluate two training algorithms and activation functions for the hidden layer available in Matlab^® version 26.1.0.3276743 (R2026a Update 3, MathWorks Inc., Natick, MA, USA). The methodology is validated by comparing hundreds of input-variable combinations generated through binomial coefficients. A case study using data from Sonora, Mexico, shows that the best model is the Collinearity–Causality (CC)-NARX-4 model, which uses four input variables, a radial basis function in the hidden layer, and Bayesian regularization backpropagation. This model achieves a root-mean-square error (RMSE) of approximately 132 watts (W) for the forecasting stage/forecasting horizon. The results are also compared with a nonlinear autoregressive (NAR) model to assess the predictive benefit of including exogenous inputs. The final outcome is a robust methodology for improving multivariable neural networks through (i) optimized input-vector selection using collinearity and causality tests, validated by an exhaustive combinatorial algorithm; and (ii) a systematic procedure for configuring the hidden-layer transfer function and the neural network training function. Full article

This contribution presents a simulation-based approach for optimizing injection molding processes using digital twins. It combines surrogate modeling via response surface methodology (RSM) with the evolutionary algorithm NSGA-II to efficiently capture complex relationships between process parameters and objectives. A key element is the adaptive enhancement of the training dataset within the decision-relevant region of interest (ADEROI) by a modified greedy max–min algorithm. This strategy closes data gaps, improves model accuracy in the potentially optimal region, and directs additional simulations to informative areas. Leave-one-out (LOO) and hold-out (HO) cross-validations show strong root mean square error (RMSE) and $R^2$ values for deformation, shrinkage, cycle time, and mass. NSGA-II converges after 403 generations and results in 191 Pareto-optimal solutions, which are consolidated into preference-consistent operating points. These points make trade-offs between analyzed objectives’ deformation, shrinkage, and cycle time explicit for process pre-design. Preferred solutions are identified through weighted sums of normalized objectives and inversely mapped process parameters. Their agreement with the physics-based digital twin at the hundredths level supports the plausibility of the selected operating points within the investigated simulation-based workflow. A retrospective benchmark against a scaled single-stage LHS baseline shows that ADEROI achieves ROI-equivalent point density with fewer simulation runs for the investigated case, reducing the estimated runtime by $39.1\%$ and resulting in a $1.64\times$ speed-up. The quantitative validation is limited to one thin-walled PP keyholder component; further geometries, mold layouts, and polymer materials are required to empirically assess generalizability. Full article

The eruption of the Tajogaite volcano (Cumbre Vieja) on La Palma Island (Spain) generated a significant amount of volcanic ash (VA). This study evaluates the valorisation of VA, considered a “natural waste,” as a partial substitute for Portland cement or in combination with lime. By using this waste, this study aims to promote its valorisation and contribute to the circular economy on the island and in nearby areas. After the ash undergoes a drying and grinding process, various tests are conducted to assess its physical, mineralogical, and chemical properties. These tests include particle size distribution, powder X-ray diffraction, and field emission electron microscopy, among others. Methods such as the Frattini test, the R³ method, thermogravimetric analysis and calorimetry are used to measure pozzolanic reactivity. The values obtained using the Frattini and R³ methods indicate that VA has low-moderate reactivity. The mechanical properties of mortar specimens based on Portland cement blends and hydrated lime are analysed, where a portion of these binders is replaced with VA. It has been observed that the compressive strengths of the specimens with 15%, 25%, and 35% of cement replaced by VA in cement blends show favourable results after 90 and 365 days of curing. Mortars with a 25% replacement reach compressive strengths exceeding 40 MPa versus 57 MPa of the control after 28 days of curing, which is adequate for many applications in civil engineering. The study highlights the importance of exploring eco-friendly materials and believes that the addition of VA can be a valuable and effective enhancement for mortars. This research marks a significant endeavour in exploring the volcanic ash produced by the Tajogaite Volcano eruption, particularly in relation to its mechanical behaviour in lime-pozzolan mortars. Full article

To clarify the effects of biochar application on leaf photosynthesis, dry matter accumulation, and productivity in a maize–soybean intercropping system, a two-year field experiment was conducted in the Yellow River irrigation area of Inner Mongolia from 2024 to 2025. A split-plot design was adopted with two biochar application rates (0 and 5 t ha⁻¹) and three cropping patterns, including maize monoculture, soybean monoculture, and maize–soybean 2:4 intercropping. Leaf SPAD values, photosynthetic characteristics (Pn, Tr, Gs, and Ci), yield components, and land equivalent ratio (LER) were determined. Compared with maize monoculture, intercropping significantly increased maize SPAD values at the V12 and VT stages by 12.80% and 13.39% in 2024 and by 15.41% and 20.58% in 2025, respectively, and enhanced maize Pn, Tr, and Gs at the V12 and R1 stages. Soybean showed greater sensitivity to intercropping, with reduced SPAD values, Pn, Tr, and Gs during the branching, flowering, and pod-setting stages, whereas biochar application partially alleviated these inhibitory effects. Intercropping increased maize kernel number per ear and thousand-kernel weight but reduced soybean effective plant density, grain number per plant, and grain yield. Biochar application improved the grain yield of both intercropped maize and soybean. Under biochar application, the LER values reached 1.04 in 2024 and 1.21 in 2025, indicating a clear advantage in land-use efficiency. Overall, biochar application and maize–soybean intercropping were associated with improved photosynthetic performance, higher land-use efficiency, and increased system productivity. Full article

Background and Objectives: Bladder cancer (BCa) is characterized by high rates of recurrence and progression, underscoring the need for reliable non-invasive biomarkers. Circular RNAs (circRNAs) are covalently closed non-coding RNAs generated by back-splicing and are stable in biological fluids, including urine. Increasing evidence implicates circRNAs in BCa pathogenesis. However, identification of clinically relevant circRNAs remains challenging. This study aimed to streamline circRNA selection and identification of functional urinary circRNAs for potential use as biomarkers for BCa monitoring. Methods: Using a database-screening approach, we identified circRNAs with high predicted affinity (TDMD score > 1.1) to miR-101-3p (a tumor-suppressive microRNA in BCa). In addition, candidate circRNAs were prioritized based on the following: (i) derivation from genes involved in BCa tumorigenesis; and (ii) origination from exonic or long non-coding RNA sequences. The potential contribution of Argonaute-2 (Ago2) binding sites to circRNA activity or potential usage as biomarker was also evaluated. Expression levels were assessed in urine samples and BCa cell lines, and functional relevance was examined using molecular and cellular assays. Results: circCIAO1(5) and circMALAT1 fulfilled prioritization criteria and exhibited distinct Ago2-binding site profiles. Both circRNAs were upregulated in urine from BCa patients and in aggressive BCa cell lines and showed differential expression between remission and recurrent disease. CircCIAO1(5) demonstrated higher-affinity binding to miR-101-3p, while both circRNAs interacted with miR-101-3p and Ago2. Functional assays revealed enhanced proliferation, motility, and invasion upon circRNA overexpression, consistent with miR-101-3p sequestration and reduction in depression of its target oncogene—EZH2. Conclusions: circCIAO1(5) and circMALAT1 are promising candidates as urinary biomarkers for noninvasive BCa monitoring, illustrating the value of bioinformatics-guided determination of circRNA as potential biomarkers and significance of circRNA-mediated regulatory mechanisms in BCa biology. Full article

Under the national context of energy transition and energy conservation, accurate prediction of thermal load in oil and gas gathering and transportation stations is crucial for ensuring operational safety and reducing energy consumption. To address the limitations of traditional forecasting methods in handling the nonlinear, non-stationary, and long-term temporal dependencies of thermal load data, this paper proposes a hybrid deep learning model that integrates convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and an attention mechanism, namely the CNN–LSTM–Attention model. First, key influencing factors such as ambient temperature, return water temperature, and the previous hour’s thermal load were selected as model inputs through correlation analysis. Subsequently, a CNN was employed to extract spatial features from multi-source data, LSTM to capture temporal dependencies, and an attention mechanism to dynamically focus on critical operational nodes, thereby enhancing the model’s ability to perceive important features. The experimental results show that the proposed model performs excellently in heat load prediction, achieving a root mean square error of 5.98, a mean absolute error of 4.66, and a mean absolute percentage error of 9.66%, with an R-squared (R²) value of 0.9568. Its prediction accuracy and stability are significantly superior to those of the standalone CNN and standalone LSTM models. This study provides an effective algorithmic solution for precise thermal load forecasting in oil and gas gathering and transportation stations and offers insights for optimizing the applicability of deep learning models in industrial scenarios. Full article

Deep neural networks have been widely applied in architectural analysis and design research, supporting tasks such as facade recognition, floor-plan analysis, and architectural visual classification. However, although existing models possess strong predictive capabilities, their decision-making processes remain characterized by a pronounced black-box nature, making it difficult to provide architects with understandable and traceable grounds for judgment. This limits their practical value in the architectural field, as designers require not only accurate outputs but also interpretable explanatory evidence regarding the basis of decision-making. This issue is particularly critical in architectural interpretation, where judgments are rarely made solely on the basis of isolated visual features, but are instead often formed through comparison and negotiation with precedents, spatial logic, and domain knowledge. To address this challenge, this paper proposes ArchiExplain, a multi-level interpretability framework for architectural image understanding, aiming to enable a deeper understanding of architectural images. The main contributions of this study are threefold: (1) We construct two architectural datasets for interpretability evaluation: a facade dataset composed of streetscape images from Harbin, China, and Greece, and a floor-plan dataset consisting of Real-plan drawings from real design cases and standardized generated R-plan drawings. Unlike existing datasets that primarily serve style recognition, semantic parsing, or image generation tasks, the datasets in this paper focus on evaluating the correspondence among model explanations, precedent associations, visual evidence, and predictive judgments. (2) Based on the above datasets, we propose the ArchiExplain framework. Unlike attribution methods such as Grad-CAM, Saliency Maps, and Integrated Gradients, which mainly reveal local discriminative regions, or influence-based methods that only trace the influence of training samples, this framework integrates training-sample influence tracing, Saliency Maps, and Integrated Gradients. It establishes a unified evidential chain among precedent samples, discriminative image regions, and final predictions, thereby transforming neural network decisions into an interpretable reasoning process with architectural significance. (3) Experimental results show that ArchiExplain performs stably on 100 randomly selected test samples, achieving an accuracy of 98.41% in the facade classification task and 98.34% in the floor-plan classification task. Further deletion/occlusion faithfulness analysis shows that the main attribution methods outperform the random baseline. Meanwhile, a questionnaire study involving 28 architects further verifies the consistency between model explanations and human architectural cognition. These findings indicate that ArchiExplain can enhance the transparency of architectural deep learning models and has practical application potential in architectural design analysis, model diagnosis, and precedent-based learning. Full article

This study investigates the effect of nano-zeolite and lime on the resistance of reconstituted soil using an integrated experimental and explainable machine learning framework. Soil samples were prepared with varying proportions of nano-zeolite, lime, and fines, and cured under controlled temperature and time conditions. Soil resistance (q) was measured to evaluate the mechanical performance of each mixture. Eight machine learning models, including artificial neural networks (ANN), random forest (RF), random tree (RT), random committee–random tree (RC-RT), M5Rules, KStar, RBFS, and additive regression–decision stump (AR-DS), were developed using Weka 3.8.6 to predict soil resistance based on the input parameters. Model performance was assessed using SSE, MAE, MSE, RMSE, Error %, Accuracy %, R², correlation coefficient, Willmott Index, Nash–Sutcliffe Efficiency, Kling–Gupta Efficiency, and SMAPE. ANN and RF achieved superior accuracy (R² ≥ 0.98) with minimal prediction error, effectively capturing the nonlinear interactions between stabilizer content, curing time, and environmental conditions. Sensitivity analyses using the analysis index and SHAP values revealed that nano-zeolite, lime, and curing time were the dominant factors influencing soil resistance, while fines content and curing temperature had secondary effects. The results demonstrate that nano-zeolite and lime significantly enhance soil resistance and that explainable machine learning models can reliably predict and interpret soil performance, providing a data-driven framework for optimized soil stabilization in geotechnical engineering applications. Full article

The subject of CO₂ subsurface storage security has never been more critical, and there is a need to explore the injection of functional materials that are capable of providing both conformance control and in situ CO₂ adsorption, thereby improving overall formation storage integrity. Herein, a molecular dynamics simulation method was used to investigate the adsorptive tendency of two variants of interpenetrating network (IPN) composite materials comprising amine-stabilized hydrophobically modified cellulose sulphates and methylene bisacrylamide crosslinked polyacrylamide. Using the COMPASS III force field and Metropolis Monte Carlo, the diffusivity and adsorption isotherms for CO₂ were determined in the IPN gels, respectively. The results indicate that the two interpenetrating networks D-I-AM-MBA-G-Cl and D-II-AM-MBA-G-Cl demonstrated reasonable CO₂ adsorption. In saline conditions, the adsorption was further enhanced with diffusion coefficients of 4.87 × 10⁻⁴ cm²/s and 2 × 10⁻⁶ cm²/s. The adsorption isotherm of D-I-AM-MBA-G-Cl closely fits the Sips equation, with a regression coefficient of 0.9996, while that of D-II-AM-MBA-G-Cl follows the Temkin isotherm with an R² value of 0.9885. This study revealed that carefully designed plugging agents with strong CO₂ adsorption tendencies can aid in the improvement of the geosequestration integrity of subsurface formations. Full article

Accurate estimation of winter wheat aboveground biomass (AGB) is essential for crop growth monitoring and precision agricultural management. To reduce the effects of canopy structural complexity and spectral saturation on AGB estimation, this study evaluated winter wheat grown under different compost substitution ratios and planting densities. Based on unmanned aerial vehicle (UAV) multispectral and RGB imagery acquired over two growing seasons at four key growth stages, spectral vegetation indices, colour vegetation indices, and canopy structural features were extracted and integrated. Recursive feature elimination, Elastic Net, and support vector regression were used to construct stage-specific AGB estimation models. The optimal feature strategy varied among growth stages, indicating that AGB estimation requires stage-specific feature selection rather than a single fixed feature combination. The proposed framework achieved validation R² values of 0.872, 0.898, 0.867, and 0.895 at the jointing, booting, flowering, and grain-filling stages, respectively, and the corresponding RRMSE values were 12.5%, 12.1%, 14.3%, and 12.0%, respectively. Additional comparisons with PLSR, RF, and XGBoost based on the stage-specific optimal feature sets further confirmed the competitive performance of SVR under the present small-sample and multi-source feature conditions. Model improvement was more evident at the flowering and grain-filling stages. At these stages, the integration of selected spectral, colour, and structural features better represented canopy closure, spike-layer formation, and late-season biomass variation. Under the treatment combining 20% compost substitution with a planting density of 4.5 million plants ha⁻¹, winter wheat maintained relatively high AGB levels across growth stages. The novelty of this study lies in demonstrating that the effectiveness of multi-source UAV feature fusion for winter wheat AGB estimation is growth-stage dependent and is enhanced when coupled with feature selection. These findings provide a methodological reference for multi-temporal AGB monitoring and precision cultivation management under similar field conditions. Full article

This study investigated the influence of the National Marine Park of Zakynthos (NMP) on overall tourist satisfaction, with a particular focus on the conservation of the endangered loggerhead sea turtle (Caretta caretta). Using a quantitative methodology on a hybrid sample of 1216 respondents, the research framework was validated via exploratory factor analysis (EFA). The measurement model analyzed visitor attitudes across two primary dimensions: ecological destination factors and institutional management factors. The multiple linear regression analysis indicated that both groups of latent factors contributed significantly to tourist satisfaction (R² = 0.359, p < 0.001). The study revealed high environmental awareness among visitors, who supported spatial–behavioural restrictions and expressed a strong willingness to contribute to protection programs through monetary donations. In conclusion, the results demonstrate that strict biodiversity conservation is not a barrier but rather a critical asset that enhances the destination’s sustainable tourism value. Full article

Global climate change is causing an increase in extreme rainfall events, which impacts the risk of hydrometeorological disasters. To support disaster mitigation and early warning systems, accurate and reliable rainfall predictions are required. Although ensemble forecasting is widely used to model atmospheric uncertainty, raw ensemble results often exhibit insufficient bias and dispersion. Therefore, post-processing techniques are needed to improve the quality of probabilistic predictions. The most commonly used calibration method is Bayesian Model Averaging (BMA). This study conducted a scoping review of peer-reviewed papers on ensemble forecast calibration using BMA, based on the PRISMA-ScR framework. Furthermore, this study presents a comprehensive bibliometric analysis involving co-authorship networks of productive authors and bibliometric maps with clustered terms. A total of 35 relevant articles were identified from 49 screened publications. The bibliometric analysis revealed that “ensemble forecasting” and “Gaussian distribution” are the most dominant terms in the research network, indicating that Gaussian-based approaches remain more widely used in ensemble forecast calibration studies. In contrast, studies explicitly applying Gamma-based approaches are still relatively limited despite their relevance for modeling asymmetric rainfall data. The results obtained in this study highlight the importance of developing and integrating more appropriate probability distributions, such as those within the Extreme Value Theory framework, into BMA models. These findings suggest that the selection of appropriate probabilistic distributions in BMA-based calibration frameworks plays an important role in improving forecast reliability and the representation of uncertainty in rainfall prediction. Furthermore, the development of more suitable probability distributions, including Extreme Value Theory (EVT)-based distributions, has strong potential to enhance probabilistic calibration performance for asymmetric rainfall data. This approach is expected to improve the accuracy and reliability of extreme rainfall predictions. The findings of this study provide an important contribution to the development of early warning systems for hydrometeorological disasters and support the achievement of Sustainable Development Goals (SDGs). Full article

Medical image registration integrates information from different types of medical images to support and improve clinical diagnosis. Existing image registration approaches are mainly classified into two categories: model-driven methods and data driven methods. Model-driven methods can achieve high registration accuracy but suffer from low computational efficiency and long processing time. In contrast, data-driven methods stand out for their high efficiency, which gives them great practical value. Taking this advantage as the core basis, this paper proposes a simple unsupervised deep learning framework embedded with a multi-scale strategy. The overall network consists of two core modules: an Affine Transformation Network (AT-Net) and a multi-scale Deformable Transformation Network (DT-Net). The multi-scale design adopted in the DT-Net enables image registration at different feature scales, which effectively improves the overall registration accuracy. In addition, a dual consistency constraint is introduced into the framework to further enhance the model robustness. The entire network realizes end-to-end medical image registration. We verified the performance of the proposed method on a public dataset, with mutual information (MI) adopted as the evaluation metric. The experimental results show that our registration algorithm outperforms several mainstream methods, including Symmetric Image Normalization (SyN), VoxelMorph (VM), the coarse-to-fine deformable transformation framework for unsupervised multi-contrast MR image registration with dual consistency constraint (C-F-I-R), TransMorph and DiffuseMorph. The comparative experiments fully demonstrate that combining the multi-scale strategy with deep learning techniques is an effective solution for medical image registration tasks. Full article