Search Results (161)

Background/Objectives: This study aimed to examine gender-associated differences in extracorporeal shock-wave lithotripsy (ESWL) utilization as well as urolithiasis diagnostic evaluation, treatment strategy, and follow-up across a multinational sample of urologists and trainees. Methods: A multinational cross-sectional survey of members of the Société Internationale d’Urologie (SIU) was conducted from June to July 2022 in seven languages. The survey was completed by 3747 urologists and trainees (283 female and 3464 male respondents) from 108 countries. To reduce baseline differences, propensity-score matching was performed with exact matching on years of clinical practice, continent, and career stage, yielding a matched cohort of 1191 respondents (279 female and 912 male). Categorical comparisons used Cochran–Mantel–Haenszel analyses stratified by a matched-set identifier, and 0–10 scored items were compared using stratified Wilcoxon rank-sum analyses with the matched set as the stratum. Results: In the matched cohort, annual procedural-volume distributions differed by gender for ureterorenoscopy (URS) (p = 0.0009), open surgery (p = 0.002), laparoscopy/robotics (p = 0.010), and percutaneous nephrolithotomy (PCNL) (p = 0.018), whereas ESWL volume did not differ significantly (p = 0.077). Among ESWL-specific categorical practices, coupling-gel use differed by gender (p = 0.029), while alpha-blocker use, antibiotic prophylaxis, JJ-stent use, stone-fragment collection, ESWL-machine type, and operator did not. Among the 851 respondents that completed the 0–10 practice items (186 female, 665 male), urine-culture assessment (8.4 vs. 7.6; p = 0.030), complete blood count (8.0 vs. 7.5; p = 0.047), and repeating ESWL within 7 days (2.5 vs. 3.4; p = 0.048) showed nominal gender-associated differences. The majority of other scored items were similar, including computed tomography (CT) urography (p = 0.055). Because these analyses were exploratory and no multiplicity adjustment was applied, nominally significant findings should be interpreted as hypothesis-generating. Conclusions: In this multinational survey of SIU members, most self-reported ESWL-specific practices were similar between female and male respondents after matching on key demographic, geographic, and professional characteristics. Gender-associated differences were observed in procedural-volume distributions as well as a limited number of reported assessments and procedural items. These findings are exploratory associations rather than causal effects of gender and may reflect measured and unmeasured professional, institutional, geographic, and training-related factors. Full article

In grouting projects, the relationship between slurry properties and mix proportions is multivariate and nonlinear. Researchers have widely employed mathematical and computational methods to investigate this relationship, and the use of machine learning to predict the properties of fly ash slurries has gradually become a hot topic. To this end, this paper uses a convolutional neural network (CNN) to establish predictive models relating grout mix proportions to the properties of the grout and the resulting stone bodies. It investigates the effects of the water-to-binder ratio, fly ash dosage, and water glass volumetric dosage on the slurry density, viscosity, initial setting time, and final setting time of fly ash grout, as well as on the compressive strength and stone rate of the corresponding stone bodies. The CNN is optimized using the information bottleneck theory and attention mechanisms, and finally, based on nine sets of self-designed experiments, compares four neural network models for predicting fly ash performance. The results show that the information bottleneck–attention dual-optimized convolutional neural network had an average prediction error of 10.26%, which represents a 44% improvement over the average prediction error of 18.38% for a single convolutional neural network. It also achieved multi-objective performance prediction for fly ash slurry. Finally, the reliability of this prediction model was verified through a combination of laboratory and field tests. Full article

Air void content (Va) is a key volumetric parameter governing the performance of Stone Mastic Asphalt (SMA), yet its prediction becomes challenging when steel slag, fibers, and additives are incorporated. This study develops an interpretable machine learning framework for predicting Va using 74 mixtures collected from 16 published studies and nine mixture-related variables. XGBoost was optimized using Particle Swarm Optimization and Grey Wolf Optimizer (GWO), with the best configuration obtained by GWO at a population size of 40 and a minimum development-stage 5-fold cross-validation (CV) RMSE of 0.371%. On the principal 70/30 evaluation partition, the optimized model achieved R² = 0.944, RMSE = 0.397%, MAE = 0.262%, and MAPE = 0.054, outperforming the evaluated benchmark models in R², RMSE, MAE, and MAPE. Robustness analyses showed that prediction accuracy was sensitive to data partitioning and literature-source composition, indicating that the reported performance should be interpreted within the represented data domain. SHAP, permutation importance, and feature-ablation analyses consistently identified binder penetration as the most influential predictor, followed mainly by asphalt content and softening point. Overall, the proposed framework provides an interpretable tool for preliminary Va estimation and mixture screening, while independent laboratory and field validation remains necessary before practical deployment. Full article

Background/Objectives: Nonsurgical treatment for pancreatolithiasis is widely performed. However, treatment success remains difficult to predict before treatment initiation, complicating the selection of an appropriate treatment strategy. This study aimed to predict pancreatic stone clearance after nonsurgical treatment for pancreatolithiasis associated with chronic pancreatitis by integrating pretreatment computed tomography (CT) images and clinical information using deep learning and machine learning models. Methods: Of 195 patients with pancreatolithiasis associated with chronic pancreatitis who underwent nonsurgical treatment, including extracorporeal shock wave lithotripsy, at our institution between 1992 and 2024, only 91 (47%) had extractable pretreatment noncontrast abdominal CT images and were included in the AI analysis. Multiple deep learning models (VGG16/19, InceptionV3, ResNet50, DenseNet121/169/201, Vision Transformer, and Swin Transformer) were trained using CT images, and their predictive performance was compared. Imaging-derived and clinical predictors selected using only the training data in each patient-level cross-validation fold were combined and used as inputs for conventional machine learning models, including random forest, support vector machine, naïve Bayes, neural network, and gradient boosting. Results: Successful pancreatic stone clearance was achieved in 54 of 91 patients (59%). Compared with the 104 patients without extractable CT data, the analyzed cohort had a higher proportion of asymptomatic pancreatolithiasis (43% vs. 15%) and a markedly lower pancreatic stone clearance rate (59% vs. 88%), indicating potential selection bias. Asymptomatic pancreatolithiasis and a pancreatic stone size of ≥15 mm, defined using a data-derived exploratory cutoff, were significantly associated with unsuccessful stone clearance. Among the deep learning models, ResNet50 achieved the highest performance (area under the receiver operating characteristic curve [AUC], 0.718), followed by Vision Transformer (Large model, 16 × 16 patches) (AUC, 0.700). When image-derived features were combined with clinical features, the neural network achieved the best performance, with a mean AUC of 0.757, a median sensitivity of 0.568, a median specificity of 0.704, and a median accuracy of 0.659. Conclusions: A neural network integrating CT-derived image features with clinical information showed moderate internal predictive performance for pancreatic stone clearance. Because this was a single-center retrospective study without external validation, the present model should be regarded as a preliminary predictive model requiring validation in independent cohorts before clinical application. Full article

This paper presents a summary of the activities carried out in relation to the shared objectives of the SCORPiò-NIDI project, an interdisciplinary research initiative aimed at investigating ballistic traces preserved along the northern fortification of Pompeii, produced by Roman siege engines during the siege of 89 BC. The study integrates digital surveying, computational modeling, experimental archaeology, and visualization techniques to document, analyze, and interpret specific types of anthropic damage caused by large stone projectiles and metal-tipped darts. High-resolution 3D acquisitions and reverse engineering processes guided the formulation of reconstructive hypotheses concerning projectile trajectories, impact velocities, and energy transfer mechanisms, supporting the determination of dimensional modules on the basis of which ancient treatises describe the proportional design of dart-throwing and stone-throwing machines in siege conditions. Mechanical simulations and comparative analysis provided quantitative validation of the virtual demonstrators prototyped and/or reconstructed using techniques and materials compatible with the pre-Christian period. The project further developed interactive digital models, animations, and visualization tools to support knowledge dissemination and public engagement. By combining archaeological data, engineering analysis, and human–computer interaction strategies, this research demonstrates the potential of digital technologies to enhance the study, interpretation, and communication of cultural heritage. Full article

Near-infrared (NIR) spectroscopy-based adulteration detection approaches are still dominated by one-dimensional (1D) spectral analysis, which inherently limits the exploration of complex patterns and nonlinear interactions in spectral data. Therefore, the objective of this study is to use a two-dimensional (2D) NIR spectrogram, combined with Explainable Artificial Intelligence (XAI), to predict the level of adulteration in palmyrah sap. The dataset matrix dimension is 110 × 1101, derived from the sample adulteration level (0–100%) and the NIR wavenumber (4000–12,500 cm⁻¹). Following Kennard–Stone partitioning, the evaluated preprocessing methods were applied using parameters derived exclusively from the training set. For the 2D modeling branch, the resulting training and testing spectra were subsequently transformed separately using the Continuous Wavelet Transform (CWT). A total of six AI algorithms, three from machine learning (PLS, kNN, ANN) and three from deep learning (CNN, AlexNet, ResNet), were applied in this study. The best model AI was interpreted using Shapley Additive Explanations (SHAP) for 1D NIRs and the Gradient-weighted Class Activation Mapping (Grad-CAM) for 2D NIR spectrograms. The four best-performing model configurations can predict the level of palmyrah sap adulteration, with R² values ranging from 0.969 to 0.994 and RMSE ranging from 2.333% to 5.547% in the training. In the testing, the model’s performance is in the R² range of 0.959–0.990, RMSE of 3.093–6.396%, MAE of 2.358–4.252%, RPD of 5.06–10.46 and Bias of 0.03–0.93%. The SHAP and Grad-CAM XAI revealed that the wavenumber associated with this sap counterfeiting is critical to the level of adulteration of palmyrah sap. This approach provides a quantitative method that accounts for advanced dimensions and treats them as essential information to support large-scale data matrices in AI modeling. The application of this method is an alternative that is easy to interpret and implement, and can be applied to long- and short-wavelength data from continuous NIR or discrete multi-wavelength NIR. Full article

An appropriate amount of active stone powder can effectively improve the performance of concrete and reduce the amount of cement. However, the doped clay in stone powder will have an adverse effect on the performance of concrete, and its contents need to be tested and strictly controlled. In this study, we have developed a new test method for evaluating the quality grade of sand powder using hyperspectral imaging. The collected hyperspectral data of stone powder are preprocessed by smoothing filtering and multiple scattering calibration (SG-MSC) to reduce the interference of background information. Competitive adaptive reweighted sampling (CARS) and the successive projections algorithm (SPA) have been used to extract the eigenvalues and display the characteristic band, respectively, and then to construct the eigenvector dataset of the spectral curve. Subsequently, a classification model combining CARS and the Support Vector Machine (SVM) algorithm is trained and applied to the quality grade identification of stone powder. Finally, a comparison has been made between the classification results of the Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) models, which combined CARS and SPA, to construct a better evaluation model. Compared with the results of manual measurement, the proposed method demonstrates a high precision in the quality assessment of stone powder content in the actual production process. Full article

To address the limitations of traditional BP neural networks in predicting manufactured sand concrete strength, specifically their susceptibility to local optima and “black-box” opacity, this study developed an integrated framework combining improved optimization algorithms with the Shapley Additive Explanations (SHAP) method. Using a dataset of 375 data points, genetic algorithm-back propagation (GA-BP) and GOOSE-BP prediction models were developed, with AutoFeat employed for explicit model construction based on a SHAP feature analysis. The results demonstrate that the GOOSE-BP model significantly outperformed traditional methods, achieving an R² of 0.916 and reducing prediction errors by 47.5%. The SHAP analysis identified paste thickness and stone powder content as the primary determinants of strength. Key thresholds were established, including a water-to-binder ratio sensitivity range of 0.35–0.50, an optimal stone powder content of 80–110 kg/m³, and a recommended sand ratio of 0.38–0.45. By converting complex nonlinear mappings into interpretable explicit expressions, this study provides a robust scientific basis and a practical computational tool for predicting concrete strength, facilitating the deep integration of machine learning with civil engineering practice. Full article

Efficient removal of stones and soil from harvested hazelnuts remains a critical challenge in postharvest processing, especially in regions where mechanization is limited. There is a growing need to optimize cleaning systems to improve grain quality, reduce labor, and support scalable operations. This study investigates the optimization of air velocity, feed rate, drop distance, and impurity mixture in a horizontal wind tunnel pneumatic separation system designed for hazelnut postharvest cleaning. Using both classical statistical analysis and Random Forest (RF) modeling, the performance metrics, grain purity, grain loss, and net contaminant removal, were evaluated across variable settings. The results reveal significant influences of air velocity and drop distance on cleaning efficiency, with optimal performance achieved at 25 m/s, 500 kg/h, and a 60–70 cm drop range. Machine learning models achieved high predictive accuracy (R² > 0.9), confirming their utility for performance forecasting. This integrated approach offers robust recommendations for machine parameter settings, supporting mechanized cleaning solutions to enhance efficiency and reduce manual labor in hazelnut production. Full article

Granite is a major lithology of stone-built cultural heritage across East Asia, the Iberian Peninsula, the Indian subcontinent, Egypt and Italy. Long regarded as durable, it nonetheless undergoes mineralogical, microstructural and macroscopic alteration through pathways that are mechanistically universal yet regionally distinctive in expression. This review synthesizes granite weathering within a multianalytical framework spanning mineralogy, microstructure, geochemistry, environmental drivers and conservation science. Mineral-specific reactions—feldspar hydrolysis, biotite oxidation coupled to clay-mineral genesis, iron-bearing transformations driving surface coloration, quartz-mediated thermal microcracking and accessory-mineral pathologies—are examined as coupled processes governing macroscopic decay. A suite of complementary analytical methods, including non-destructive, minimally invasive and laboratory-based techniques, delivers mechanistic and prognostic resolution unattainable by any single method. Two case settings—the tenth-century rock-carved Buddhas of Gyeongju Namsan and the urban granite of Jongmyo Shrine, Seoul—illustrate how integrated diagnostics resolve coupled decay on natural outcrops and how cumulative atmospheric exposure is recorded in monument-scale fabrics. Chemical weathering indices, environmental controls and conservation implications are unified into a single framework, and key gaps—standardization, time-resolved diagnostics, climate projection, multi-omics coupling, consolidant durability and machine learning—are articulated as a research agenda for granite heritage science. Full article

Chronic Kidney Disease of Unknown Etiology (CKDu) is an ongoing global health concern, particularly affecting agricultural communities in equatorial regions. Unlike traditional chronic kidney disease (CKD), CKDu occurs without common risk factors such as diabetes, hypertension, or kidney stones. Its etiology remains poorly understood, with environmental exposures, occupational hazards, and genetic susceptibility proposed as contributing factors. Omic technologies including genomics, transcriptomics, proteomics, metabolomics, and exposomics offer promising avenues to elucidate CKDu pathogenesis by enabling comprehensive molecular profiling and identification of biomarkers. Recent genomic studies have explored single nucleotide polymorphisms (SNPs) linked to kidney injury susceptibility, while transcriptomic analyses have identified differential expression of genes involved in oxidative stress and tubular injury pathways. Proteomic investigations have revealed candidate urinary biomarkers such as heat shock proteins and inflammatory mediators, and metabolomic profiling has highlighted alterations in amino acid and energy metabolism in affected individuals. Exposomic approaches are beginning to characterize cumulative chemical exposures, including pesticides and heavy metals, in endemic regions. This narrative review synthesizes current evidence on the application of omics approaches in CKDu research, highlights knowledge gaps, and proposes future directions for integrating multi-omics studies with machine learning and artificial intelligence approaches. Advancing omics-based investigations may provide critical insights into disease mechanisms, improve diagnostic precision, and inform targeted interventions for vulnerable populations. Full article

Ground improvement for foundations supported on soft soils is traditionally problematic because of low bearing capacity and a large magnitude of settlement. One sustainable method for mitigating these problems is the use of stone columns (SCs), particularly geosynthetic-encased stone columns (GESCs), to improve load transfer, confinement, and consolidation. This review critically synthesizes recent advances in the analysis and design of SC systems using experimental investigations, numerical simulations, and machine learning (ML)-based methodologies. The article indicates that GESCs, when integrated with modern data-driven techniques, especially hybrid metaheuristic ML models, represent a reliable and sustainable solution for soft soil stabilization. Traditional analytical and empirical methods remain useful; however, they are often inadequate for very soft soils (Undrained shear strength (c_u) < 15 kPa), where excessive bulging and large deformations dominate system behavior. Consequently, intelligent hybrid modeling approaches are emerging as the next generation of optimized, data-driven design tools in geotechnical engineering. Different failure mechanisms of SCs, including bulging, punching shear, and general shear failure, are critically discussed along with the governing design parameters. Previous studies consistently indicate that spacing ratios within the range of s/D = 2–3 can improve the bearing capacity ratio (BCR) by approximately 50–100%. Numerical and experimental studies further demonstrate that SC systems can transfer nearly 60–80% of the applied load through stress concentration and soil arching mechanisms. Furthermore, the application of geosynthetic encasement enhances the performance of SCs in very soft soils by increasing confinement, reducing lateral deformation, and enhancing bearing capacity by nearly 3–6 times compared with ordinary SCs. The review also evaluates the growing role of artificial intelligence techniques in forecasting settlement and bearing capacity behavior. ML techniques such as artificial neural networks (ANN), support vector regression (SVR), random forest (RF), XGBoost, and hybrid metaheuristic–ML models have shown high predictive capability, often achieving prediction errors below 5%. Despite these advancements, many existing ML studies still suffer from limited datasets, a lack of generalization, and insufficient incorporation of physical mechanisms. Full article

Soil organic carbon (SOC) is closely linked to soil fertility, agricultural carbon cycling, and the functioning of cotton field ecosystems, and it provides essential information for sustainable soil management. Rapid and accurate SOC estimation is therefore important for assessing carbon sequestration potential and supporting low-carbon agricultural management. This study focused on cotton fields in northern Shawan City and used optical imagery, Synthetic Aperture Radar (SAR) imagery, and 140 ground-collected SOC samples to estimate SOC content with three machine learning models: Random Forest (RF), Light Gradient Boosting Machine (LightGBM), and Extreme Gradient Boosting (XGBoost). The Kennard–Stone algorithm was applied to partition the 140 SOC samples into training and validation subsets at a 7:3 ratio, ensuring a more representative distribution of samples. Model performance was evaluated using the coefficient of determination (R²) and root mean square error (RMSE), and SHapley Additive exPlanations (SHAP) was used to interpret feature contributions and SOC spatial variability. The results showed that: (1) optical features performed better than SAR features, while fused optical-SAR features achieved the highest accuracy; (2) XGBoost consistently outperformed RF and LightGBM, with the optimal model achieving R² = 0.726 and RMSE = 1.252% on the validation set; (3) SHAP analysis confirmed the dominant contribution of optical features to SOC estimation; and (4) the predicted SOC distribution showed higher values in the central study area, lower values in the northern and southern parts, and high-value zones mainly along both sides of the Manas River. By comparing optical, SAR, and fused features for SOC estimation in arid-zone cotton fields, this study provides methodological support for rapid SOC monitoring and sustainable soil management, and offers practical guidance for variable-rate fertilization and soil carbon sequestration planning along the Manas River corridor. Full article

This study proposes and evaluates a hybrid framework for predicting curling stone motion by combining physical models with machine learning. Motion capture data from a curling sheet were used to train modules for sliding trajectory prediction and post-impact collision prediction. These modules were connected in an integrated rollout from a single preprocessed-frame state 1 m before the tee line to predict resting position and in-play/out-of-play status. Huber regression was used for trajectory prediction and random forest regression for collision prediction, with hybrid variants learning residual corrections to physical-model outputs. The framework was evaluated using five-fold cross-validation. In trajectory prediction, ML and hybrid variants reduced velocity error relative to the default physical model, while the tuned physical model remained competitive for direction-angle estimation. In collision prediction, ML and hybrid models improved direction-angle and angular-velocity prediction over the perfectly elastic baseline. In the integrated simulation, 867 trials were evaluated after excluding 21 trials with both measured stones out of play. The hybrid rollout achieved the lowest stop-position MAE and SD for the colliding stone and, for the collided stone, an MAE comparable to that of the ML model with the lowest SD. These results show that residual correction of simple physics-based baselines improves local prediction and final-position stability. Full article

Background: Emergency department evaluation of suspected urolithiasis increasingly relies on non-contrast CT, yet not all patients require imaging. Existing clinical prediction rules help stratify stone probability, but by converting continuous measurements into fixed binary indicators, they offer little insight into why a particular patient is at risk or how much uncertainty remains after each testing stage—questions that bear directly on individualized diagnostic decisions. Methods: We retrospectively analyzed 1000 ED patients with suspected urolithiasis who underwent non-contrast CT (stone prevalence 85.0%). A gradient boosting classifier was trained on 17 continuous clinical and laboratory features and compared against binary-thresholded counterparts and an established scoring system; the 17-feature model achieved AUC 0.771 (95% CI 0.726–0.813) versus 0.723 (95% CI 0.675–0.771) for the reference score on this cohort (DeLong p = 0.001). Individual predictions were explained using an interventional Shapley value approach, and a Shannon entropy-based framework was applied to quantify the marginal diagnostic contribution of each sequential testing stage. Results: Held-out permutation importance identified red blood cell count on microscopy, age, pain duration, and prior stone history as the most influential predictors. Several features showed non-linear contributions that diverged from conventional binary thresholds: creatinine effect crossed zero near 0.90 mg/dL and pain duration peaked between 2 and 5 h. C-reactive protein, absent from existing scoring systems, emerged as a meaningful negative predictor. Sequential entropy analysis showed that dipstick urinalysis provided the largest marginal information gain among non-history stages (6.1% of prior entropy), while physical examination contributed 2.3%. A prevalence sensitivity analysis projected that the framework’s threshold behavior would differ substantially in lower-prevalence populations, underscoring that the cohort-specific cut-points are not portable decision rules. We therefore position the framework as a reasoning aid that complements clinical judgment and imaging, not as a stand-alone triage tool. Conclusions: Explainable machine learning can address questions that aggregate discrimination metrics cannot: which features drive risk for a given patient, how those effects behave across the continuous measurement range, and how much diagnostic uncertainty each testing stage resolves. The Shapley-based explanations and entropy framework developed here offer a structured approach to individualized diagnostic reasoning in the ED evaluation of suspected urolithiasis, functioning as an interpretive adjunct to, rather than a replacement for, existing clinical tools and CT imaging. Full article