Search Results (192)

In froth flotation, the features of froth images are important information to predict the concentrate grade. However, the froth structure is influenced by multiple factors, such as air flowrate, slurry level, ore properties, reagents, etc., which leads to highly complex and dynamic changes in the image features. Additionally, issues such as the immeasurability of ore properties and measurement errors pose significant uncertainties including aleatoric uncertainty (intrinsic variability from ore fluctuations and sensor noise) and epistemic uncertainty (incomplete feature representation and local data heterogeneity) and generalization challenges for prediction models. This paper proposes an uncertainty quantification regression framework based on cross-modal interaction fusion, which integrates the complementary advantages of Selective Kernel Networks (SKNet) and Vision Transformers (ViT). By designing a cross-modal interaction module, the method achieves deep fusion of local and global features, reducing epistemic uncertainty caused by incomplete feature expression in single-models. Meanwhile, by combining adaptive calibrated quantile regression—using exponential moving average (EMA) to track real-time coverage and adjust parameters dynamically—the prediction interval coverage is optimized, addressing the inability of static quantile regression to adapt to aleatoric uncertainty. And through the localized conformal prediction module, sensitivity to local data distributions is enhanced, avoiding the limitation of global conformal methods in ignoring local heterogeneity. Experimental results demonstrate that this method significantly improves the robustness of uncertainty estimation while maintaining high prediction accuracy, providing strong support for intelligent optimization and decision-making in industrial flotation processes. Full article

The comprehensive evaluation of factors that increase the difficulty of autonomous driving in various complex traffic situations and diverse roadway geometries within living lab environments is of great interest, particularly in developing sustainable urban mobility systems. This study introduces a novel methodology for assessing autonomous driving vulnerabilities and identifying urban traffic segments susceptible to autonomous driving risks in mixed traffic situations where autonomous and manual vehicles coexist. A microscopic traffic simulation network that realistically represents conditions in a living lab demonstration area was used, and twelve safety indicators capturing longitudinal safety and vehicle interaction dynamics were employed to compute an integrated risk score (IRS). The promising weighting of each indicator was derived through decision tree method calibrated with real-world traffic accident data, allowing precise localization of vulnerability hotspots for autonomous driving. The analysis results indicate that an IRS-based hotspot was identified at an unsignalized intersection, with an IRS value of 0.845. In addition, analytical results were examined comprehensively from multiple perspectives to develop actionable improvement strategies that contribute to long-term sustainability, encompassing roadway and traffic facility enhancements, provision of infrastructure guidance information, autonomous vehicle route planning, and enforcement measures. Furthermore, this study categorized and analyzed the characteristics of high-risk road sections with similar geometric features to systematically derive effective traffic safety countermeasures. This research offers a systematic, practical framework for safety evaluation in autonomous driving living labs, delivering actionable guidelines to support infrastructure planning and validate sustainable autonomous mobility. Full article

Amid accelerating climate change, intensifying urban heat island effects, and rising public demand for livable, walkable streets, there is an urgent practical need for interpretable and actionable evidence on streetscape quality. Yet, research on streetscape quality has often relied on single data sources and linear models, limiting insight into multidimensional perception; evidence from temperate monsoon cities remains scarce. Using Tianjin’s main urban area as a case study, we integrate street-view imagery with remote sensing imagery to characterize satellite-derived environmental indicators at the point scale and examine the following five perceptual outcomes: comfort, aesthetics, perceived greenness, summer heat perception, and willingness to linger. We develop a three-step interpretable assessment, as follows: Elastic Net logistic regression to establish directional and magnitude baselines; Generalized Additive Models with a logistic link to recover nonlinear patterns and threshold bands with Benjamini–Hochberg false discovery rate control and binned probability calibration; and Shapley additive explanations to provide parallel validation and global and local explanations. The results show that the Green View Index is consistently and positively associated with all five outcomes, whereas Spatial Balance is negative across the observed range. Sky View Factor and the Building Visibility Index display heterogeneous forms, including monotonic, U-shaped, and inverted-U patterns across outcomes; Normalized Difference Vegetation Index and Land Surface Temperature are likewise predominantly nonlinear with peak sensitivity in the midrange. In total, 54 of 55 smoothing terms remain significant after Benjamini–Hochberg false discovery rate correction. The summer heat perception outcome is highly imbalanced: 94.2% of samples are labeled positive. Overall calibration is good. On a standardized scale, we delineate optimal and risk intervals for key indicators and demonstrate the complementary explanatory value of street-view imagery and remote sensing imagery for people-centered perceptions. In Tianjin, a temperate monsoon megacity, the framework provides reproducible, actionable, design-relevant evidence to inform streetscape optimization and offers a template that can be adapted to other cities, subject to local calibration. Full article

Local pier scour remains one of the leading causes of bridge failure, calling for predictions that are both accurate and uncertainty-aware. This study develops an interpretable data-driven framework that couples CatBoost (Categorial Gradient Boosting) for deterministic point prediction with NGBoost (Natural Gradient Boosting) for probabilistic prediction. Both models are trained on a laboratory dataset of 552 measurements of local scour at bridge piers using non-dimensional inputs (y/b, V/V_c, b/d₅₀, Fr). Model performance was quantitatively evaluated using standard regression metrics, and interpretability was provided through SHAP (Shapley Additive Explanations) analysis. Monte Carlo–based reliability analysis linked the predicted scour depths to a reliability index β and exceedance probability through a simple multiplicative correction factor. On the held-out test set, CatBoost offers slightly higher point-prediction accuracy, while NGBoost yields well-calibrated prediction intervals with empirical coverages close to the nominal 68% and 95% levels. This framework delivers accurate, interpretable, and uncertainty-aware scour estimates for target-reliability, risk-informed bridge design. Full article

Crashworthiness refers to a structure’s ability to absorb and dissipate impact energy through controlled deformation, thereby enhancing protection of vehicle occupants and onboard equipment. Composite materials possess significant potential in crashworthy airborne and ground vehicle structures due to their favourable specific energy absorption. However, their performance depends on several design factors such as materials, stacking sequences, and geometry. To reduce development costs and time to market, numerical simulations have become a necessary tool for optimising these factors. A challenge in this approach is the calibration of models, which is decisive for ensuring reliable and predictive simulations. Among other approaches, Non-local Damage Models have demonstrated reliability in simulating crashworthy composite structures. This work presents the intralaminar fracture energy calibration of fabric ply within a Waas–Pineda model, as implemented in ESI Virtual Performance Solutions, using Compact Tension and Compact Compression tests. Full article

In arid regions characterized by extreme water scarcity, it is important to synergistically optimize both crop yield and water use. Irrigation strategies based on empirical knowledge overlook crops’ dynamic water needs and may cause water waste and yield loss. To address this issue, this paper proposes an intelligent irrigation scheduling method based on a crop growth model and an improved deep reinforcement learning (DRL) agent. We construct a high-fidelity cotton growth environment using the Decision Support System for Agrotechnology Transfer (DSSAT) model. The model was calibrated with local data from the Shihezi region, Xinjiang, to provide a reliable simulation platform for DRL agent training. We developed a temporal state representation module based on a Bidirectional Long Short-Term Memory (BiLSTM) network and an attention mechanism. This module captures dynamic trends in historical environmental information to focus on critical decision factors. The Soft Actor–Critic (SAC) algorithm was improved by integrating a feature attention mechanism to enhance decision-making precision. A dynamic reward function was designed based on the critical growth stages of cotton to incorporate agronomic prior knowledge into the optimization objective. Simulation results demonstrate that our proposed method can improve water use efficiency (WUE) by 39.0% (with an 8.4% increase in yield and a 22.1% reduction in water consumption) compared to fixed-schedule irrigation strategies. An ablation study further confirms that each of our proposed modules—BiLSTM, the attention mechanism, and the dynamic reward—makes a significant contribution to the final performance. Full article

Understanding the strain rate behavior of rock materials is key to geomechanical engineering. However, in numerical tools such as the Particle Flow Code (PFC), the chosen bonded-particle contact model also fundamentally dictates the mechanical response. A systematic comparison of how quasi-static strain rates affect different contact models, Parallel-Bonded (PBM), Soft-Bonded (SBM), and Flat-Jointed (FJM), using a common calibration baseline, has been lacking. This study addresses that gap by first calibrating all three models against identical laboratory data from the siltstone of Paleozoic-aged Trakya formation in Cebeciköy-Istanbul, Türkiye. Subsequently, numerical uniaxial loading simulations were conducted on the calibrated models at three distinct quasi-static strain rates (0.01, 0.005, and 0.001 s⁻¹) to compare their stress–strain response, crack evolution, and failure patterns. The results demonstrate that while the initial elastic stiffness was largely insensitive to the applied strain rates across all models, the post-peak behavior and failure mechanism remained fundamentally distinct and model dependent. PBM consistently produced an abrupt, localized brittle failure, SBM exhibited more gradual softening with distributed tensile damage, and FJM displayed the most widespread, mixed-mode failure pattern. It is concluded that within the quasi-static loading conditions, the intrinsic formulation of the chosen contact model is a more dominant factor in controlling the failure style, damage localization, and post-peak characteristics than the specific strain rate applied. Full article

A touch-off explosion on concrete slabs is considered one of the simplest yet most destructive forms of adversarial loading on building elements. It causes far greater damage than explosions occurring at a distance. The impact is usually concentrated in a small area, leading to surface cratering, scabbing of concrete, and even tearing or rupture of the reinforcement. Studies available on the behavior of reinforced concrete (RC) slabs under touch-off (contact) and standoff explosions commonly indicate that the maximum damage occurs when the blast is applied to the center of the slab. This observation raises an important question about how the position of an explosive charge, especially relative to the free edge of the slab, affects the overall damage pattern in slabs supported on only two sides with clamped supports. This study uses a modeling strategy combining Eulerian and Lagrangian domains using the finite element tools of Abaqus Explicit v2020 to examine the behavior of a square slab supported on two sides with clamped ends subjected to blast loads at different positions, ranging from the center to the free edge and beyond, under touch-off explosion conditions. The behavior of concrete was captured using the Concrete Damage Plasticity model, while the reinforcement was represented with the Johnson–Cook model. Effects of strain rate were included by applying calibrated dynamic increase factors. The developed numerical model is validated first with experimental data available in the published literature for the case where the explosive charge is positioned at the slab’s center, showing a very close agreement with the reported results. Along with the central blast position, five additional cases were considered for further investigation as they have not been investigated in the existing literature and were found to be worthy of study. The selected locations of the explosive charge included an intermediate zone (between the slab center and free edge), an in-slab region (partly embedded at the free edge), a partial edge (partially outside the slab), an external edge (fully outside the free edge), and an offset position (250 mm beyond the free edge along the central axis). Results indicated a noticeable transition in damage patterns as the detonation point shifted from the slab’s center toward and beyond the free edge. The failure mode changed from a balanced perforation under confined conditions to an asymmetric response near the free edge, dominated by weaker surface coupling but more pronounced tensile cracking and bottom-face perforation. The reinforcement experienced significantly varying tensile and compressive stresses depending on blast position, with the highest tensile demand occurring near free-edge detonations due to intensified local bending and uneven shock reflection. Full article

Accurate estimation of actual crop evapotranspiration ($E{T}_a$) is crucial for effective irrigation management, especially in regions facing growing water scarcity. This study evaluates the performance of the Simple Algorithm for Evapotranspiration Retrieving (SAFER) in a Mediterranean citrus orchard using remote sensing and Eddy Covariance (EC) data. The model was calibrated using local flux tower data from 2021 to 2022. The results show strong agreement between observed and modeled $E{T}_a$ during the wet season, with excellent statistical metrics (R² = 0.89 and 0.85; r = 0.95 and 0.92; RMSE = 0.95 mm day⁻¹ and 0.91 mm day⁻¹; bias = −0.94 mm day⁻¹ and 0.53 mm day⁻¹ for 2021 and 2022, respectively), confirming the reliability of SAFER under well-watered conditions. However, the model performance decreased significantly during the dry season, R² = 0.352 and 0.167; r = −0.593 and 0.408; RMSE = 0.86 mm day⁻¹ and 0.68 mm day⁻¹; bias = 0.01 mm day⁻¹ and 0.38 mm day⁻¹ for 2021 and 2022, respectively, likely due to the limited capacity of vegetation indices to detect plant physiological stress under water deficit conditions. SAFER detected spatial variability in $E{T}_a$ across the orchard, highlighting its potential for irrigation zoning. Comparisons with studies in tropical and semi-arid regions demonstrated consistency in mid-season $E{T}_a$ estimates, supporting the model’s adaptability. Despite reduced accuracy under drought conditions, SAFER remains a cost-effective and reliable tool for $E{T}_a$ monitoring during optimal growth periods. Overall, it shows strong potential as a remote sensing-based tool for sustainable crop management, though dry-season applications require additional stress-adjustment factors. Full article

Various intersection structures are utilized in city-wide traffic network infrastructure by local transportation authorities to handle the exponentially increasing traffic loads in developing countries. In this regard, numerous studies have considered the notable positive contribution of the modern roundabouts in intersection performance as a prominent method utilized widely in our contemporary world. Properly designed roundabouts are vital components of sustainable transportation planning, as they significantly influence traffic efficiency, safety, and environmental performance. Accurate estimation of roundabout capacity is essential to ensure that they can accommodate anticipated traffic volumes without causing congestion, thereby contributing to energy efficiency and reducing emissions. Moreover, sustainable roundabout design supports the development of safer and more inclusive transportation networks by improving accessibility for all road users, thus strengthening the overall sustainability of urban mobility. The SIDRA (version 8.0), a traffic simulation software, is frequently employed in performance analysis and determining the effects of possible outcomes of different scenarios of roundabouts in today’s world. On the other hand, driver behaviors are found to play a significant role in software performance during the analysis process of roundabout capacity and performance. Therefore, in order to optimize the environmental factor (EF) representing driver behaviors in the SIDRA software, a Differential Evolution Algorithm-Based Bi-Level Calibration Model (DEBCAM) was introduced. Observation data collected from eight different modern-structured roundabouts through drones were run into the SIDRA simulation software; the average delays obtained were employed to estimate optimum EF values through DEBCAM. Observed average delay values were taken into consideration with respect to the delay values obtained as a result of the SIDRA calibration by using the GEH statistics. GEH values indicate the consistency of vehicle delay data obtained via the DEBCAM with observed data. Acquired results clearly suggest that the SIDRA software needs to be calibrated so that it can represent drivers’ behaviors. After determination of the optimum values of the EF parameter for calibration of the SIDRA software, the regression analysis was conducted through the Partial Least Squares (PLS) method. As a result of the analysis, a capacity estimation model was developed, which displayed a significant conformity with the SIDRA capacity estimation results. Our findings suggested that the parameter requirement for the roundabout capacity estimation can be decreased by employing the appropriate EF value for the roundabout that needs to be analyzed. Full article

Urban mobility plays a critical role in ensuring resilience during natural disasters such as floods, yet developing reliable traffic models remains challenging for medium-sized cities with limited monitoring infrastructure. This study developed a hybrid traffic modeling approach that integrates Google Traffic data with field measurements to address incomplete digital coverage in flood-prone areas of Loja, Ecuador. The methodology involved collecting 1501 field speed measurements and 235,690 Google Typical Traffic observations using exclusively open-source tools and freely available data sources. Adjustment factors ranging from 0.25 to 0.97 revealed systematic discrepancies between Google Traffic estimates and field observations, highlighting the need for local calibration. The resulting traffic network model encompassing 4966 nodes and 5425 edges accurately simulated flood impacts, with the most critical scenario (Thursday 17–19, 100% road impact) showing travel time increases of 1123% and congestion index deterioration from 1.79 to 21.69. Statistical validation confirmed significant increases in both travel times (p = 0.0231) and distances (p = 0.0207) under flood conditions across five representative routes. This research demonstrates that accurate traffic models can be developed through intelligent integration of heterogeneous data sources, providing a scalable solution for enhancing urban mobility analysis and emergency preparedness in resource-constrained cities facing climate-related transportation challenges. Full article

The Yellow River Basin faces severe challenges in water security and ecological protection: at the basin scale, complex hydrological processes and fragile ecosystems undermine the water security pattern; at the local scale, waterlogging risks have intensified in Zhengzhou—a core city in the lower reaches—impacting the city itself and also exerting negative effects on the basin’s water security. To address this, mapping the scientific layout of green infrastructure (GI) is urgent. However, existing studies on GI layout at the basin-urban scale have certain limitations: neglect of underlying surface spatial heterogeneity, insufficient integration of natural, hydrological and social factors’ synergies, and lack of research on large-scale basins and cities, especially ecologically sensitive areas with complex hydrological processes. To fill these gaps, this study proposes an integrated framework (SCS–GIS–MCDM) combining the SCS hydrological model, GIS spatial analysis, and multi-criteria decision making (MCDM). The SCS hydrological model is refined via localized parameter calibration for better accuracy; indicator weights are determined through the MCDM framework; and green infrastructure (GI) suitability maps are generated by integrating ArcGIS spatial analysis with fuzzy logic. Results show that (1) 6.8% of Zhengzhou is highly suitable for GI, mainly in riparian areas and the Yellow River alluvial plain; (2) sensitivity analysis confirms flooded areas and runoff corridors as key drivers; (3) spatial validation against government-issued ecological control zone plans demonstrates the model’s value in balancing flood safety and socio-economy. This framework provides a replicable application model for GI construction in cities along the Yellow River Basin, thereby supporting urban planners in making evidence-based decisions for sustainable blue–green space planning. Full article

To improve the physical consistency and interpretability of traditional drought indices, this study proposes a drought assessment model that couples physically based constraints with data-driven approaches, leading to the development of a Multivariate Drought Index (MDI). The model employs convolutional neural networks to achieve physically consistent downscaling, thereby obtaining a high-resolution Normalized Difference Water Index (NDWI), Temperature Vegetation Dryness Index (TVDI), Vegetation Condition Index (VCI), and Temperature Condition Index (TCI). Objective weights are determined using the Criteria Importance Through Intercriteria Correlation method, while random forest and Shapley Additive Explanations are integrated for nonlinear interpretation and physics-guided calibration, forming an ensemble framework that incorporates multi-source and multi-scale factors. Validation with multi-source data from 2000 to 2024 in the major maize-growing areas of Heilongjiang Province demonstrates that MDI outperforms single indices and the Vegetation Health Index (VHI), achieving a correlation coefficient (r = 0.87), coefficient of determination (R² = 0.87), RMSE (0.08), and classification accuracy (87.4%). During representative drought events, MDI identifies signals 16–20 days earlier than the Standardized Precipitation Evapotranspiration Index (SPEI) and the Soil Moisture Condition Index (SMCI), and effectively captures localized drought patches at a 250 m scale. Feature importance analysis indicates that the NDWI and TVDI are consistently identified as dominant factors across all three methods, aligning physically interpretable analysis with statistical contribution. Long-term risk zoning reveals that the central–western region of the study area constitutes a high-risk zone, accounting for 42.6% of the total. This study overcomes the limitations of single indices by integrating physical consistency with the advantages of data-driven methods, achieving refined spatiotemporal characterization and enhanced overall performance, while also demonstrating potential for application across different crops and regions. Full article

Metabolic syndrome (MetS) is a complex condition characterized by a group of interconnected metabolic abnormalities. Due to its increasing prevalence, better predictive markers are needed. Therefore, this study aims to develop predictive models for MetS by integrating adipokines, metabolic and cardiovascular risk factors, and anthropometric indices. Data were collected from 381 subjects aged 20 to 59 years (242 women and 139 men) from Guadalajara, Jalisco, Mexico, who were classified as having MetS or non-MetS based on the ATP-III criteria. Four supervised machine learning models were developed—Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost)—and their performance was evaluated using the Area under the Curve (AUC), calibration curves, Decision Curve Analysis (DCA), and local interpretability analysis. The RF and XGBoost models achieved the highest AUCs (0.940 and 0.954). The RF and LR models were the best calibrated and showed the highest net benefit in DCA. Key variables included age, anthropometric indices (BRI and DAI), insulin resistance measures (HOMA-IR), lipid profiles (sdLDL-C and LDL-C), and high-molecular-weight adiponectin, used to classify the presence of MetS. The results highlight the usefulness of specific models and the importance of anthropometric variables, cardiovascular risk factors, metabolic profiles, and adiponectin as indicators of MetS. Full article

This study aimed to evaluate short-term clinical outcomes in COVID-19 pneumonia patients using parameters derived from a commercial deep learning-based automatic detection algorithm (DLAD) applied to serial chest radiographs (CXRs). We analyzed 391 patients with COVID-19 who underwent serial CXRs during isolation at a residential treatment center (median interval: 3.57 days; range: 1.73–5.56 days). Patients were categorized into two groups: the improved group (n = 309), who completed the standard 7-day quarantine, and the deteriorated group (n = 82), who showed worsening symptoms, vital signs, or CXR findings. Using DLAD’s consolidation probability scores and gradient-weighted class activation mapping (Grad-CAM)-based localization maps, we quantified the consolidation area through heatmap segmentation. The weighted area was calculated as the sum of the consolidation regions’ areas, with each area weighted by its corresponding probability score. Change rates (Δ) were defined as per-day differences between consecutive measurements. Prediction models were developed using Cox proportional hazards regression and evaluated daily from day 1 to day 7 after the subsequent CXR acquisition. Among the imaging factors, baseline probability and ΔProbability, ΔArea, and ΔWeighted area were identified as prognostic indicators. The multivariate Cox model incorporating baseline probability and ΔWeighted area demonstrated optimal performance (C-index: 0.75, 95% Confidence Interval: 0.68–0.81; integrated calibration index: 0.03), with time-dependent AUROC (Area Under Receiver Operating Curve) values ranging from 0.74 to 0.78 across daily predictions. These findings suggest that the Δparameters of DLAD can aid in predicting short-term clinical outcomes in patients with COVID-19. Full article