Search Results (1,206)

A 56-day feeding trial was designed to investigate the effects of alginate oligosaccharide (AOS) on the growth, immune response, antioxidant activity and apoptosis pathways of largemouth bass (Micropterus salmoides). We formulated six isonitrogenous and isoenergetic diets with different concentrations of AOS (0% (control), 0.05%, 0.1%, 0.15%, 0.2% and 0.25%). The results showed that the WGR of the AOS0.15–0.2 groups were markedly increased, and the FBW and SGR of the AOS0.1–0.2 groups were also markedly boosted. In addition, no significant differences were observed in FCR, SR and FI in the treatment groups. According to SGR and WG second-degree polynomial regression analysis, the optimum AOS addition level for juvenile largemouth bass was 0.14–0.15%. On the other hand, no notable differences were observed in crude protein, moisture, crude lipid or crude ash content between groups, and no notable differences were also observed in the levels of AST and ALT in the plasma between all groups. Additionally, ALP activities were considerably higher in the AOS0.15–0.25 groups. In terms of intestinal digestion and absorption function, AOS0.1 group and AOS0.15 group significantly increased the intestinal amylase and lipase activities, and A0S0.1–0.2 groups significantly increased the intestinal trypsin activities, while proper dietary supplementation with AOS significantly improved villus muscular thickness, villus height, and villus width. Furthermore, proper dietary supplementation with AOS significantly up-regulated the mRNA levels of occ, clau, C6A6, C7A5, C7A8B, C6A14 and pept1 in the intestine. No significant differences were observed in the mRNA levels of C7A6, C7A1A and C7A10A between all groups. With respect to the antioxidant and immune functions of the intestine, the analysis revealed no remarkable differences between the groups concerning SOD, GPX activity or T-AOC content in the intestine. However, a significant increase in CAT activity of the intestine was observed in the AOS0.05–0.15 groups, and MDA levels were lower in all AOS-added groups. Apart from the above, AOS0.15–0.25 groups significantly reduced intestinal TNF-α concentration. No notable differences were observed in the intestinal contents of TGF-β, IL-10 and IL-6 between all groups. Additionally, proper dietary supplementation with AOS could improve antioxidant effects and inhibit inflammation by regulating the gene expressions of the related-Nrf2 and NF-κB signaling pathway, including nrf2, keap1, Mn-sod, gpx, nf-κb, il-10 and tgf-β. There was no significant difference in the mRNA levels of cat, fox, il-8 and tnf-α. With respect to cell apoptosis in the intestine, TUNEL assay results showed that green positive cells were significantly lower in the AOS0.05–0.2 groups than the AOS0 group. Additionally, proper dietary supplementation with AOS could inhibit cell apoptosis by regulating the mRNA levels of bxl-xl, caspase 3, caspase 8, caspase 9 and bcl-2. However, there was no significant effect on the level of bax mRNA in any of the treatment groups. In summary, proper dietary supplementation with AOS exerted positive effects on growth, intestinal digestion and absorption function, immune antioxidant responses, and apoptosis pathways to a certain extent. Full article

Machine learning (ML) algorithms can be used to extract clinically meaningful information from movement data captured by inertial measurement units (IMUs), but many human activity recognition (HAR) pipelines are developed on large laboratory datasets that may not reflect small, heterogeneous, real-world samples. The purpose of this study is to systematically compare the accuracy of multiple ML models, feature sets (both simple and expanded), class balancing strategies, null and transition period handling techniques, and sensor configurations for recognizing a set of four everyday activities extracted from IMU time series data from an age-diverse population. Six ML classifiers were trained and tested: multilayer perceptron, random forest, k-nearest neighbors, logistic regressor, CatBoost, and gaussian naive bayes. These approaches were used in a pipeline with differing sampling techniques including the synthetic minority oversampling technique or random undersampling, and feature handling steps including principal component analysis or a Select-From-Model metatransformer. Additionally, two deep learning methods, DeepConvLSTM and ResGCNN, were trained and tested. Accuracy, precision, recall, and area under the receiver operating characteristic curve were compared to a dummy classifier as a benchmark approximation to chance performance. All pipelines performed better than the dummy classifier, with model accuracy ranging between 0.427 and 0.644. This study demonstrated the ability of several ML algorithms to properly recognize a set of functional activities using limited IMU data from both children and adults. Full article

Municipal sewage sludge contains considerable amounts of protein, making protein recovery a viable route for sludge valorization. In this work, sludge disintegration was achieved by cyclone cutting coupled with ozone oxidation, and the mixed liquor of foam standing liquid and supernatant was used as the feedstock for protein recovery via isoelectric point precipitation. Pretreatment tests showed that under 60 mg/L ozone concentration, 10 °C and 60 min, alkaline conditions enhanced sludge lysis; the mixed liquor suspended solids (MLSS) removal rate reached 87.65% at pH 9, and the protein concentration in the foam layer reached 1530.14 mg/L at pH 11, yielding a protein-rich feedstock suitable for subsequent extraction. In the isoelectric point precipitation stage, single-factor and L9(3⁴) orthogonal experiments were conducted to examine the effects of pH, temperature and centrifugal speed on extraction rate, and four ensemble learning algorithms (GBR, RF, XGBoost and CatBoost) were employed to build prediction models. The results showed that the factor influence order was pH > centrifugal speed > temperature, with pH being extremely significant (p < 0.01). Under leave-one-out cross-validation, the XGBoost model performed best (R² = 0.9243, MAE = 2.78%, RMSE = 3.52%). Response surface analysis determined the optimal parameters as pH 4.0, 5 °C and 3500 r/min, with both predicted and measured precipitation-stage extraction rates of 86.19%. Amino acid analysis indicated that essential amino acids accounted for 39.9% of the extracted protein, with good rehydration and foaming stability. Ensemble learning algorithms can reveal the multi-factor nonlinear coupling in isoelectric point precipitation, providing data support for process optimization of sludge protein recovery. Full article

The emergence of resistance-associated mutations in coronavirus main protease (Mpro) poses a significant challenge to the development of broad-spectrum antiviral therapeutics. In this study, we improved and accelerated a mutation-aware machine learning (ML) framework to predict the binding score of Nirmatrelvir analogue ligands against wild-type and mutant MERS-CoV Mpro. A library of 15,889 Nirmatrelvir derivatives generated through systematic scaffold modification was docked against the wild-type and five variants of the Mpro, producing a total of 95,334 structural and docking score datasets of these protein–ligand complexes. During the ML model development phase, ligand effects were learned from RDKit molecular descriptors and graph-based representations, and the mutation-induced effects were captured through delta-encoded physicochemical properties (hydrophobicity, charge, aromaticity, and polarity) of the active-site residues. Among the evaluated models, the CatBoost regressor tree-based algorithm achieved the lowest mean absolute error (MAE) value of 0.23 Kcal/mol and an R² of 0.87. Further improvement was achieved by creating a weighted ensemble model combining the CatBoost regressor, XGBoost and LightGBM regressor, resulting in a prediction accuracy with a MAE of 0.19 Kcal/mol and an R² of 0.90 relative to docking scores. Model robustness was further evaluated through random-, ligand group- and scaffold group- K-fold cross-validation along with their Y-randomization. Moreover, the models were also tested with a new set of 1000 structurally diverse compounds. SHAP analysis was conducted, which identified 20 molecular descriptors critical for accurate predictions. The ensemble model accurately predicted the binding affinities of Nirmatrelvir and its four analogues (E1–E4), reproducing the experimental pIC50 trend and correctly identifying the most potent inhibitors. The ensemble model also showed consistent performance across all MERS-CoV Mpro variants, S147Y, S142G, L144A, S142G/S147Y, and S142G/L144A/S147Y, demonstrating its potential for rapidly discovering mutation-resistant antiviral drugs. Full article

The construction sector faces a critical need to minimize its carbon footprint, which is currently stimulating the development of geopolymer concrete using recycled coarse aggregates as an eco-friendly material compared with Portland cement. Accurate prediction of the compressive strength of this eco-efficient concrete is complex, however, as a result of the complex, non-linear interactions between many of the mix-design and curing parameters. Although modern scientific literature and engineering practices have increasingly adopted machine learning (ML) for concrete strength prediction, a significant scientific gap remains. Most existing studies rely on “black-box” models that lack sufficient interpretability and frequently overlook the severe risk of data leakage during validation, limiting their practical engineering application. To address this gap, this study proposes a robust, data-leakage-aware framework driven by a rigorous nested GroupKFold cross-validation strategy. By grouping concrete samples by their unique Mix_ID, this approach ensures genuine generalization to entirely unseen mixtures. Within this reliable validation scheme, the CatBoost algorithm is utilized for compressive-strength prediction, with the Dung Beetle Optimizer (DBO) serving as an effective tool for hyperparameter tuning. The evaluation results across multiple random seeds show that the DBO–CatBoost model significantly outperforms the default CatBoost, rigorously tuned baseline models (Support Vector Regression and Random Forest), and a comparative metaheuristic benchmark (PSO–CatBoost). It achieves the most stable distribution of errors and excellent predictive accuracy (Test ${\mathrm{R}}^2=0.9995\pm 0.0002$, RMSE = $0.3828\pm 0.0909$). In addition, the model predictions were demystified using the methods of SHapley Additive exPlanations (SHAP) and partial dependence plots (PDPs). The interpretability analysis revealed strong statistical associations, showing that Curing Time and Coarse Aggregate are the most prominent predictive features and the strongest pairwise interaction between each other; the NaOH molar concentration is the most important second-level influence on optimization of strength. Overall, the framework provides a robust data-driven screening tool that can assist in preliminary mix-design evaluation. By reducing the reliance on extensive empirical “trial and error” approaches, this predictive model supports more efficient material usage and facilitates preliminary optimization of low-carbon concrete formulations. Theoretically, this study advances the fundamental science of geopolymer materials by explicitly quantifying the complex, non-linear interactions between alkaline activators, curing conditions, and recycled aggregates. This provides a robust data-driven theoretical foundation for designing and optimizing next-generation eco-friendly concrete products and structures. Full article

Deep exploration in mature gold districts requires subsurface alteration evidence that can be related quantitatively to three-dimensional (3D) geological architecture. This study develops a workflow for the Tudui–Shawang deposit in the Muping–Rushan metallogenic belt that integrates drill-core short-wave infrared (SWIR) spectroscopy, 3D alteration modeling, ore-controlling geological constraints, positive–unlabeled (PU) learning, and ensemble prospectivity prediction. A total of 2140 spectra from 10 drillholes were processed to identify mineral assemblages, extract spectral scalars and feature-shape attributes, classify alteration facies, and construct continuous 3D alteration evidence. Discrete smooth interpolation and indicator kriging were used for continuous and categorical attributes, respectively, and CatBoost, LightGBM, XGBoost, and Random Forest were evaluated within a spatially separated PU-bagging design. Quantitative analyses show that individual SWIR attributes have weak deposit-scale relationships with Au grade. Nevertheless, local IC minima, relatively lower pos2200 values near several mineralized intervals, alteration-facies transitions, and a broader shift toward longer pos2250 wavelengths characterize relevant parts of the mineralized system. FUSE performed best under 1 km × 1 km spatial holdout validation, with an ROC AUC of 0.8900 and a PRAUC of 0.8926. Prediction-area analysis and the 3D probability volume delineated three ranked exploration targets (T1–T3). The results show that drill-core SWIR-derived 3D alteration evidence, when integrated with ore-controlling geology and spatially validated machine learning, provides a practical basis for target prioritization in mature gold districts. Full article

In the construction industry, a large portion of energy is spent on heating and cooling, which both increases costs and contributes to resource depletion. The aim of the study was to provide and evaluate a robust ML model evaluation process for early design stage cooling load prediction of buildings. For this purpose, 18 different machine learning models were evaluated using a Nested Cross-Validation approach consisting of 50 outer fold and 50 inner Optuna trials, along with hyperparameter optimization. To avoid model selection being dependent on small decimal differences, paired model comparisons, effect sizes, Holm-corrected statistical tests, and the 1-SE economy rule were applied over the same outer folds. As a result of the analysis, Categorical Boosting (CatBoost) was selected as the final model, and within the Nested-CV framework, R² = 0.8275 ± 0.0072, RMSE = 1.6792 ± 0.0210 kWh, MAE = 1.4356 ± 0.0233 kWh, and MAPE = 0.0521 ± 0.0009 were obtained. Model interpretability analyses showed that the variables Ambient Temperature, Solar Radiation, and Heat Reflective Treatment had the highest permutation importance values. Residual analyses revealed that the model exhibited low systematic bias, but the residual variance was dependent on the estimate value, and the residuals deviated from a normal distribution. This study provides a framework that evaluates not only the prediction performance but also model selection, generalization stability, interpretability, and residual behavior together. The findings demonstrate that CatBoost is a strong option for cooling load prediction in this simulation-based dataset. However, validation of the obtained results with real building data and different climatic conditions is considered an important requirement for future studies in terms of evaluating the external validity of the model. Full article

Soil erosion is a major environmental issue that threatens watershed ecological security and the sustainable use of land resources. Quantifying its spatiotemporal variability and associated environmental controls is important for sustainable land use planning and watershed management. To characterize the spatiotemporal variation in CSLE-simulated soil erosion and the relative explanatory contributions of environmental variables in the Qingyi River Basin, this study integrated rainfall, soil type, digital elevation model, land use, and vegetation coverage data for six observation years from 2000 to 2025 with GIS spatial analysis and the Chinese Soil Loss Equation (CSLE). Geodetector and CatBoost–SHAP were further applied to evaluate the explanatory contributions and interaction patterns of the selected environmental variables on the simulated erosion results. The results showed the following: (1) Woodland and cropland dominated the land use structure of the basin, while construction land increased from 90.95 km² to 164.21 km². Land use patterns differed markedly between the upstream and downstream areas, with woodland and grassland dominating the upstream area and cropland and construction land accounting for higher proportions in the downstream area. (2) Across the six observation years, the mean soil erosion modulus ranged from 135.74 to 378.39 t·km⁻²·a⁻¹, indicating generally low erosion levels, with the highest value occurring in 2015 and the lowest in 2025. (3) Soil erosion intensity was mainly characterized by slight and mild erosion, which together accounted for more than 95% of the basin area, whereas areas of moderate erosion and above were mainly concentrated in downstream mountainous areas and along both sides of river valleys. (4) The explanatory analysis showed that elevation, land use, and vegetation coverage made relatively high contributions to the spatial variability in the CSLE-simulated erosion results. Topographic and vegetation-related variables showed higher explanatory contributions in the upstream area, whereas land use showed a higher contribution in the downstream area. These findings provide a quantitative basis for soil erosion monitoring, the identification of priority areas for soil and water conservation, sustainable land use optimization, and region-specific watershed management in the Qingyi River Basin. Full article

Background/Objectives: Early identification of complicated appendicitis in children remains challenging. We developed and internally validated laboratory-based machine-learning models for severity stratification using age and routine admission laboratory data. Methods: This retrospective study included 628 children with surgically confirmed appendicitis treated between 2020 and 2024. Complicated appendicitis was defined by operative or pathological evidence of perforation, gangrene, abscess, phlegmon, diffuse peritonitis, or comparable advanced inflammation. Fifteen candidate predictors were evaluated using five prespecified models. Models were tuned in the training set and evaluated once on an isolated test set. Pairwise DeLong comparisons, decision curve analysis, SHAP, and permutation importance were performed. Results: Complicated appendicitis occurred in 93 patients (14.8%). The prespecified primary CatBoost model achieved a ROC AUC of 0.867, a precision-recall AUC of 0.677, a sensitivity of 0.750, a specificity of 0.863, a positive predictive value of 0.488, and an F1-score of 0.592. Formal comparisons did not demonstrate statistically significant AUC superiority over the other algorithms after Holm correction. Exploratory decision curve analysis showed a greater net benefit than treat-all and treat-none strategies across threshold probabilities of 0.06–0.35. ESR, age, CRP, and CRP-derived indices were the most influential model features. Conclusions: Routine laboratory data may provide adjunctive information for severity stratification, but the modest event count, limited positive predictive value, and absence of external validation preclude stand-alone clinical use. Full article

Septoria leaf blotch (SLB), caused by Zymoseptoria tritici, is one of the most destructive foliar diseases of wheat and requires accurate methods for early detection and disease severity assessment. This study evaluated the potential of hyperspectral ASD measurements and UAV multispectral imagery combined with machine learning for the detection and quantification of SLB in winter wheat. Six spectral datasets derived from hyperspectral reflectance, UAV multispectral imagery, and vegetation indices were analyzed using CatBoost, Random Forest, and XGBoost algorithms. Random Forest achieved the highest classification performance, reaching an accuracy of 0.9583 and a balanced accuracy of 0.9483. For disease severity prediction, the best performance was obtained using ASD-derived vegetation indices with Random Forest (R² = 0.9199), while CatBoost consistently provided high regression accuracy across hyperspectral datasets. A reduced set of green, red, red-edge, and near-infrared bands produced classification results comparable to those obtained with the full hyperspectral spectrum, indicating that most diagnostic information is concentrated within these spectral regions. Although UAV multispectral data showed lower accuracy than hyperspectral measurements, particularly for disease severity prediction, they enabled effective field-scale disease monitoring. These findings demonstrate that hyperspectral sensing provides a valuable reference for developing accurate disease detection models, whereas UAV multispectral imagery represents a practical and scalable solution for operational precision agriculture. Full article

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

Pavement mix design for steel slag relies largely on empirical Marshall tests requiring numerous specimens and lengthy cycles. To address this, an inverse mix design (IMD) framework combining machine-learning forward prediction with grey wolf optimization (GWO) was developed. A dataset of 300 samples with 13 input features and 2 output indicators was compiled. Three algorithms—XGBoost, CatBoost, and random forest (RF)—were compared, and model interpretability was analyzed using SHAP and ALE. CatBoost achieved the best overall performance. SHAP identified steel slag f-CaO content and replacement ratio as the dominant factors governing moisture susceptibility. GWO search errors for all three design scenarios were below 0.24%. Laboratory validation showed a mean deviation of 1.02% between target and measured values, confirming the method’s feasibility. The method also supports sustainable pavement engineering by facilitating higher steel slag utilization, contributing to CO₂ reduction and natural aggregate conservation. Full article

Accurate identification of land subsidence types is essential for effective urban risk management, yet remains challenging due to the superposition of deformation signals at different spatial scales and the complexity of urban environments. In this study, land subsidence types in Fuzhou were identified through an integrated framework combining multi-scale deformation analysis and ensemble learning. Ground deformation time-series measurements were derived from 66 Sentinel-1A synthetic aperture radar (SAR) observations acquired between January 2018 and June 2023 using the time-series interferometric synthetic aperture radar (TS-InSAR). Deformation values in decorrelated areas were subsequently reconstructed using regression models driven by multi-source geological, hydrological, land-use, and urban features, resulting in a spatially continuous deformation field. A Fast Fourier Transform (FFT)-based Butterworth filtering approach was then applied to separate regional-scale and local-scale subsidence signals. Based on the extracted local deformation patterns and discriminative auxiliary features, land subsidence was classified into five categories: farmland-related subsidence, linear infrastructure-related subsidence, low-lying stratum-related subsidence, land-use transition-related subsidence, and older building area-related subsidence. Three ensemble learning models, XGBoost, CatBoost, and LightGBM, were implemented for subsidence type classification. All models achieved satisfactory performance, among which LightGBM exhibited the best overall performance. The classification results reveal pronounced differences in spatial distribution and deformation intensity among subsidence types. Farmland-related subsidence occupies the largest proportion of the affected area but is characterized by relatively moderate deformation rates, whereas older building area-related subsidence, despite its limited spatial extent, exhibits the highest deformation intensity. This study demonstrates the potential of ensemble learning for land subsidence type classification. Full article

Reliable coastal wetland habitat mapping is often constrained by the scarcity and the cost of reliable reference data, especially in data-limited coastal environments. We propose a validation-controlled, label-efficient framework pairing multi-season Sentinel-1 and Sentinel-2 predictors with a CatBoost teacher and a lightweight MLP student. A candidate is pseudo-labeled only when both separately calibrated models agree and exceed class-specific thresholds; accepted labels are class-balanced and down-weighted. The framework was evaluated at the Sidi Moussa–Oualidia wetland complex and Merja Zerga lagoon in Morocco. At Sidi Moussa–Oualidia, 62 configurations were compared through nested polygon-grouped validation and then frozen before a five-seed held-out evaluation. The supervised MLP and Agreement-augmented MLP achieved mean Macro-F1 values of $0.9518\pm 0.0044$ and $0.9509\pm 0.0062$, indicating that augmentation did not materially change the already strong full-data baseline. Under a stricter budget of 30 training and 20 validation observations per class, Agreement yielded a mean Macro-F1 of $0.9092\pm 0.0102$ compared with $0.9023\pm 0.0093$ for the supervised baseline and produced pseudo-labels in all five seeds. A spatial-range sensitivity analysis further showed that both models retained Macro-F1 values of 0.9391 and 0.9403 for test observations located beyond the largest estimated within-class autocorrelation range. At Merja Zerga, the native six-class supervised MLP achieved $0.9456\pm 0.0050$, compared with $0.9401\pm 0.0047$ after Agreement augmentation. Spatially blocked four-class experiments nevertheless showed that 20 to 30 local training labels per class recovered approximately 96–98% of the corresponding full-data performance. The framework therefore supplies an operational criterion for using unlabeled observations: augmentation is adopted only where calibrated filtering yields adequate class coverage, and validation confirms a downstream effect; otherwise the supervised model is retained. For the strict Sidi Moussa–Oualidia reduced-label experiment, the reported development budgets count every site-specific label used for fitting, early stopping, and calibration. The Merja Zerga blocked experiments separately quantify training-label sensitivity while retaining their blocked validation resources. Full article

Tropospheric evaporation ducts can trap radio waves within a refractive layer, which may guide signals above 1 GHz, enabling beyond-line-of-sight transmission. This makes duct-assisted propagation attractive for maritime communications. The marine environment is characterized by complex hydrometeorological variability and frequent extremes, particularly tropical cyclones, which can perturb duct properties and degrade link reliability. This study develops a multivariate cyclone-aware nonlinear regression framework (CNRF) to estimate contemporaneous evaporation duct strength (EDS) by integrating high-resolution dropsonde observations with tropical-cyclone descriptors from the International Best Track Archive for Climate Stewardship (IBTrACS). The framework uses CatBoost, natural-gradient boosting (NGBoost), and a multilayer perceptron (MLP) as base learners, with a random forest (RF) serving as the second-stage nonlinear fusion model. Rather than relying solely on bulk physical parameterization, the framework aims to represent the nonlinear influence of tropical cyclone-related environmental factors on duct strength. Evaluated over 1996–2024, the CNRF attains a test-set R² of 0.791 and a root mean square error (RMSE) of 5.350 M-unit, corresponding to a 23.5% improvement in RMSE over the Naval Postgraduate School (NPS) numerical model. For Hurricane Fiona (2022), the model achieves an RMSE of 6.260 M-unit, and the inclusion of tropical cyclone descriptors improves RMSE by approximately 17.0% relative to a model that excludes tropical cyclone information. The proposed framework facilitates quantitative assessment of extreme-weather-driven duct variability and supports robust design and operation of duct-enabled maritime communication systems. Full article