Search Results (3,984)

In the future, agriculture will need better irrigation management options to produce more food and decrease its air and water pollution contributions. Hydroponic systems conserve water over field production, but up to 50% of applied irrigation could be discharged from open-drain systems. TVPD is an evapotranspiration model developed for greenhouse production, particularly for hydroponics. In this study, we calibrate and evaluate TVPD on environmental and evapotranspiration data from hydroponic tomato production and compare predictions to those of random forest (RF) and K-nearest neighbors (KNN). Using five time-ordered data splits, we sought to gauge prediction accuracy for data-limited settings, where the model needs to be implemented with the least calibration time possible, and we evaluated TVPD, RF, and KNN with a 10-fold cross-validation to assess overall model robustness. Across the five data splits, TVPD produced more accurate predictions (r²: 0.86 to 0.90; RMSE: 0.1739 to 0.5796 L tray⁻¹) than RF (r²: 0.06 to 0.73; RMSE: 0.7354 to 2.0505 L tray⁻¹) and KNN (r²: 0.06 to 0.59; RMSE: 0.7694 to 1.7090 L tray⁻¹). With calibration on only the first five days of data, TVPD was able to produce acceptable predictions (r² = 0.87, RMSE = 0.5796 L tray⁻¹). The mean r² for a 10-fold cross-validation was 0.81 for TVPD, 0.88 for RF and 0.81 for KNN, and mean RMSE values were slightly better for the cross-validation for RF (0.4970 L tray⁻¹) and KNN (0.4968 L tray⁻¹) than for TVPD (0.5922 L tray⁻¹). Overall, TVPD could be a useful model to predict evapotranspiration for irrigation management and could decrease the volume of discharged hydroponic waste solution. Full article

In this study, a layer-wise point cloud representation of CT scan images is proposed, from which persistence diagrams are constructed and persistent entropy is computed as a compact topological feature for three-class lung tumor classification. Two parallel approaches are investigated: the direct computation of persistence diagrams from CT images, and computation from subsampled point clouds derived from image intensity layers. The proposed method is evaluated on the publicly available IQ-OTH/NCCD lung cancer dataset, comprising 1097 CT scan images from 110 individuals, annotated by expert oncologists and radiologists. Classification is performed using K-Nearest Neighbors (KNN), Random Forest, Support Vector Machine, and eXtreme Gradient Boosting, and compared against Convolutional Neural Network (CNN) and traditional image feature-based methods. The persistent entropy approach applied to layer-wise subsampled point clouds achieves 97.67% accuracy, a Precision–Recall AUC of 96.63%, and a ROC-AUC of 99.46% using KNN, outperforming direct image-based analysis (95.91%) and achieving comparable accuracy to the CNN method (97.21%) with a computational speedup of approximately $478\times$. These results demonstrate that persistent homology applied to subsampled point clouds provides an accurate, mathematically interpretable, and computationally efficient alternative to deep learning for lung tumor classification. Full article

In sparse representation learning-based linear k-nearest neighbors methods, the linear representation assumption frequently fails when applied to nonlinear distributed data, leading to degraded generalization and a loss of physical interpretability. To address this, we propose the Kernelized Manifold-Optimized Linear Nearest Neighbor (KMOLNN) method. Methodologically, KMOLNN projects the data into a high-dimensional kernel space to capture the nonlinear relationships, while introducing an adaptive manifold-preserving regularization term—via an adaptive Laplacian matrix—to dynamically preserve the local geometric structures. Theoretically, this study provides a mathematical proof of the nearest neighbor group effect for the kernel framework and reveals that its weight optimization behavior implicitly implements the Bayesian decision rule. Furthermore, we derive a rigorous generalization error bound using Rademacher complexity to validate its theoretical robustness. Empirically, we evaluate KMOLNN on 15 small-to-medium-scale benchmark datasets against eight comparative methods, including recent variants. The results demonstrate significant numeric superiority, with KMOLNN achieving an average accuracy of 90.76% and a Macro F1-score of 88.62% across the evaluated datasets. Finally, we present a comprehensive runtime analysis, explicitly acknowledging that these gains in generalization capability and theoretical interpretability present a practical trade-off, requiring increased computational runtime due to the iterative alternating optimization process. Full article

Consuming fast food draws consumers’ attention to emerging issues related to such consumption. Namely, the consumption of fast food affects environmental sustainability, healthy living, and other sustainable activities. The main objective of this study is to explore how environmental awareness, healthy living, and sustainability-oriented fast-food stimuli may influence neurophysiological response patterns during food-related cognitive processing. Eighteen voluntary subjects, aged 19 to 53 years, who frequently consume fast food and have no physical or mental disorders, took part in the experiment. An experiment was conducted in which data were collected using Electroencephalography (EEG) and analyzed with WinEEG. The waves detected from brain activity signals were digitally converted to data using WinEEG. The resulting digital data was further analyzed using Detrended Fluctuation Analysis, Neural Networks (NN) algorithms, and K Nearest Neighbors (k-NN) algorithms. Herewith, the findings suggest that fast-food-related visuals associated with healthy living may elicit stronger patterns of cognitive engagement among participants. The findings provide exploratory insights into implicit cognitive engagement associated with healthy-living and sustainability-related fast-food stimuli. Additionally, the discussion helps in understanding sustainability-oriented food perception and consumer neuroscience research. Full article

Phosphate waste rock piles (PWRPs) generated by open-pit phosphate mining are highly heterogeneous and difficult to characterize using conventional point sampling alone, which limits representative resource assessment, selective recovery, and rehabilitation planning. This study develops an integrated framework combining PRISMA spaceborne hyperspectral imagery, ground-based mineralogical analyses, and spatially constrained machine learning to map lithological heterogeneity at the Benguerir phosphate mining site, Morocco. A three-stage spectral optimization workflow, including atmospheric band masking, Savitzky–Golay filtering, and analysis of variance (ANOVA)-based feature selection, was applied to identify the most discriminative Short-Wave Infrared (SWIR) bands for lithological classification. After removing redundant observations located within shared PRISMA pixel footprints, 127 spatially independent samples were retained for model development. Five supervised classifiers (Random Forest, Extra Trees, XGBoost, Support Vector Machine, and K-Nearest Neighbors) were evaluated under a spatially constrained cross-validation framework aligned with the 30 m native PRISMA pixel size. Ensemble-based models, especially Extra Trees and Random Forest, provided the most stable performance, with balanced accuracies of 0.56–0.69 and area under the receiver operating characteristic curve (AUC) values exceeding 0.95 for carbonate-dominated lithologies. Lower discrimination between phosphate and siliceous facies reflects intrinsic mineralogical mixing and spectral overlap at the sensor scale. Entropy-based uncertainty and posterior probability mapping revealed spatially structured prediction ambiguity concentrated along lithological boundaries and transitional zones, consistent with petrographic evidence of compositional heterogeneity. These results indicate that moderate but stable accuracies likely represent realistic performance limits for spaceborne hyperspectral mapping of complex mining environments under spatial constraints. The proposed framework provides a transferable and uncertainty-aware basis for lithological mapping, selective recovery assessment, and sustainable phosphate waste management. Full article

Solar chimneys represent an effective passive ventilation technology capable of improving indoor thermal comfort while reducing building energy consumption. In this study, the thermal and fluid dynamic performance of a solar chimney integrated into a residential building located in Bordj Bou Arréridj (Eastern Algeria) was investigated through a comprehensive numerical, predictive, and optimization framework. A transient mathematical model was developed to evaluate the influence of key geometric parameters, including chimney width and inlet opening width, as well as environmental factors such as solar radiation intensity and wind speed, on the system performance. The generated simulation database was subsequently employed to develop and compare four machine learning models, namely, Artificial Neural Networks with Bayesian Regularization (ANN-BR), Deep Neural Networks optimized by Improved Grey Wolf Optimization (DNN-IGWO), k-Nearest Neighbors (KNN), and Extreme Gradient Boosting (XGBoost), for predicting eight output parameters including glazing temperature, fluid temperature, absorber temperature, outlet temperature, thermal efficiency, air change rate (ACH), mass flow rate, and outlet velocity. The results demonstrated that increasing chimney and inlet widths significantly enhances ventilation performance by increasing airflow rate and ACH. Weather conditions and wind speed were also found to strongly affect thermal efficiency and buoyancy-driven airflow. Among the predictive models, XGBoost and DNN-IGWO exhibited the highest predictive accuracy, achieving coefficients of determination ($R^2$) close to unity and very low prediction errors for all output variables, confirming their robustness and generalization capability. The proposed methodology provides a reliable tool for rapid performance prediction and design optimization of solar chimney systems under different climatic and operating conditions, thereby supporting the development of energy-efficient passive ventilation strategies for residential buildings. Full article

Low-cost sensors (LCSs) are increasingly used in air quality monitoring because of their affordability and scalability; however, their limited accuracy necessitates reliable calibration approaches. Although machine learning (ML)-based calibration methods have shown promising results, direct comparisons of hyperparameter optimization (HPO) strategies remain challenging due to differences in datasets, search spaces, and optimization budgets. In this study, ML models and HPO methods were evaluated within a standardized experimental framework developed on the AQ-MultiCal platform. Grid Search (GS), Random Search (RS), and Bayesian Optimization (BO) were implemented using identical hyperparameter search spaces and equal iteration budgets across both short-term and long-term real-world CO₂ datasets obtained from five low-cost NDIR-based sensors. The results showed that tree-based models achieved strong baseline performance, whereas the k-nearest neighbors (kNN) model demonstrated the greatest improvement after optimization. The optimized kNN model reduced the average RMSE from 77.4 ppm to 54.4 ppm for the short-term dataset and from 37.3 ppm to 26.2 ppm for the long-term dataset. Although the HPO methods achieved comparable predictive accuracy, substantial differences were observed in computational cost. The proposed framework enables fair and reproducible comparison of HPO strategies while balancing predictive performance and computational efficiency in real-world sensor calibration applications. Full article

This study addresses the challenge of modeling the complex non-linear relationship between process parameters and relative density in selective laser melting (SLM) of IN718 nickel-based superalloy under small-sample conditions. A data-driven prediction framework integrating data augmentation, physics-informed feature engineering, machine learning, and model interpretability analysis was developed and systematically validated. Fourteen sets of experimental data covering both vertical and horizontal building directions were collected by varying laser power (P), scan speed (v), and hatch spacing (h). To overcome the small-sample limitation, three augmentation strategies—radial basis function (RBF) interpolation, generative adversarial network (GAN), and K-nearest neighbors (KNN)—were systematically compared under unified physical constraints combining local perturbation and volumetric energy density (E_vol) filtering, with Pearson correlation coefficient consistency used to select the optimal strategy. Eight physically meaningful input features were constructed, including E_vol and line energy density (E_line), explicitly embedding SLM process physics into the learning framework. Support vector regression (SVR), random forest (RF), and artificial neural network (ANN) models were trained and their hyperparameters were systematically optimized via exhaustive grid search combined with leave-one-out cross-validation (LOO-CV), ensuring robust model selection under small-sample constraints. A physics-based baseline model (E_vol quadratic fitting, LOO-CV average R² = 0.2534) was established to quantify the gain of machine learning over empirical formulas. LOO-CV results show that ANN achieves the highest average R² of 0.9269, followed by SVR (0.9148) and RF (0.8393), all of which substantially outperform the physical baseline. Feature importance analysis reveals that E_vol accounts for 51.58% of the predictive power, and ablation experiments confirm that introducing physics-derived features improves the average R² by 0.0246 compared with raw process parameters alone. To further elucidate the predictive mechanism of the optimal ANN model, Partial Dependence Plot (PDP) analysis was conducted for all eight input features, visualizing their marginal effects on predicted density and confirming physical consistency with SLM mechanisms. This framework provides a reliable, interpretable, data-driven solution for intelligent SLM process optimization with limited experimental data. Full article

We present a 3D laser-scanning method for the fast, accurate dimensional inspection of large high-speed-rail precast box girders. The pipeline uses low-pass filtering plus sequential registration to suppress noise, and voxel filtering with curvature-aware enhancement to reduce point cloud size by 3–5× while preserving key geometry. Reconstruction employs K-nearest-neighbors and PCA to detect boundaries and curvature jumps, B-spline fitting with moving least squares for surface completion, and CSS corner detection to extract key dimensions at millimeter precision. Field tests report absolute errors ≤ 2.0 mm versus manual measurement, validating the method for automated, digital acceptance. Full article

Background/Objectives: Computed tomography (CT)-based radiomics shows promise for non-small cell lung cancer (NSCLC) prognosis prediction, but model performance varies widely by feature selection and machine learning strategies. Optimal combinations remain unclear. This study aims to systematically compare feature selection methods and machine learning algorithms for 12-month overall survival prediction using CT radiomics in NSCLC patients. Methods: We analyzed 385 patients from The Cancer Imaging Archive (TCIA) NSCLC-Radiomics dataset. Radiomic features from primary tumor volumes were combined with clinical variables. Three feature selection methods—sequential forward selection (SFS), maximum relevance minimum redundancy (mRMR), and least absolute shrinkage and selection operator (LASSO)—were compared across five classifiers: k-nearest neighbors (KNN), support vector machine (SVM), random forest (RF), logistic regression (LR), and gradient boosting classifier (GBC). Performance was assessed using area under the receiver operating characteristic curve (AUC) and accuracy on independent test sets. Cox regression and Kaplan–Meier analyses evaluated survival risk stratification. Results: Logistic regression showed the most stable classification performance across feature selection strategies (test AUC 0.60–0.65, accuracy 0.72–0.73). The mRMR-LR model achieved highest AUC (0.65); LASSO-LR showed highest accuracy (0.73). For survival analysis, LASSO-based Cox modeling demonstrated superior risk stratification with significant separation between high- and low-risk groups in both training and testing sets (p = 0.0095). Conclusions: Simpler models like logistic regression provide robust performance in CT radiomics, while LASSO excels for survival risk stratification. As we employed single-dataset validation, clinical applicability remains limited because validation was performed within a single public dataset. Nevertheless, the findings provide methodological insights into the selection of feature selection and machine learning strategies for CT radiomics-based prognostic modeling in NSCLC. Full article

Movie genre classification is a significant challenge in narrative analysis, as traditional methods often fail to capture complex structural relationships within movie stories. This study introduces the Intra-Cluster Weighted Movie Network (ICWMN), a novel framework designed to improve classification by using intra-movie relationships through Graph Neural Networks (GNNs). We constructed a large-scale dataset of 1631 movie character networks using an automated pipeline comprising web scraping, regular expressions, and fine-tuned BERT models for entity recognition. To address the computational limitations of fully connected models, we partition ICWMN into clusters and establish edges only between the k-most similar nodes using the K-Nearest Neighbor algorithm and various distance measures, such as the Laplacian and NetLSD. XGBoost is applied to optimize high-dimensional node feature vectors. Experimental results demonstrate outstanding performance, with the Graph Attention Network (GAT) emerging as the top-performing architecture, resulting in classification accuracies that peak at $95.00\%$ on our 1631-movie dataset and an exceptional $97.30\%$ on the 773-movie Moviegalaxies dataset. These findings confirm that prioritizing spectral properties and cluster-based network topologies significantly improve the precision and stability of genre classification compared to state-of-the-art methods. Full article

This study aims to provide insight into consumer purchasing decisions by integrating eye-tracking data with forecasting techniques. First, the study investigates how consumption motives (hedonic vs. utilitarian) and purchasing purposes (for oneself vs. for others) influence visual attention and decision-making processes. An experimental design was conducted with 128 participants in a simulated online shopping environment, where eye-tracking data were collected based on fixation counts and durations across defined Areas of Interest (AOIs). Second, a total of 20 input features were collected, comprising fixation counts and fixation durations for 10 review-related Areas of Interest (AOIs), and these features were evaluated across the experimental scenarios, while the binary output variable represented the participant’s purchase decision. These biometric features, together with scenario information, were used to forecast purchasing decisions using six machine-learning methods, including Artificial Neural Networks, Random Forest, Support Vector Machine, K-Nearest Neighbors, Naive Bayes, and Logistic Regression. The results indicate that consumers’ visual attention aligns with their consumption motives and purchasing purposes, revealing distinct gaze patterns across different scenarios. In the forecasting phase, the accuracy of different methods for predicting purchasing decisions using review-related eye-tracking data is evaluated. Support Vector Machines achieved the highest overall accuracy, approximately 59–60% across the evaluated datasets, compared with a validation-specific majority-class baseline of 53.85%. This corresponds to a modest improvement of approximately 5.15–6.15 percentage points over the naive benchmark. Overall, the findings suggest that objectively recorded review-related eye-tracking data can be operationalized as behavioral input features in a machine-learning-based purchase-decision classification framework, highlighting the methodological value of integrating eye-tracking insights with consumer behavior forecasting. Full article

Traditional handmade papers from China, Japan, and Korea, including Xuan paper, Washi, and Hanji, are difficult to distinguish visually because they share cellulose-rich compositions and similar appearances. This study applied near-infrared hyperspectral imaging (NIR-HSI) and machine-learning classifiers to identify selected traditional handmade papers by country and product type. Spectra in the 1250–1700 nm region were analyzed using k-nearest neighbors, support vector machines, and artificial neural networks. The models achieved high classification performance, with F1-scores of up to 1.000, and Y-scrambling confirmed that the results were not attributable to random class assignment. SHAP analysis identified important wavelength regions near 1256, 1360, 1404, 1449, 1537, 1576, 1635, and 1685 nm, which were associated with C–H, O–H, phenolic, hydrogen-bonded polysaccharide, and lignin-related vibrations. These bands varied among paper groups and provided chemically meaningful information for classification, while SAM visualization revealed pixel-level spectral similarity. These results show that NIR-HSI provides a compact, nondestructive, and interpretable approach for classifying selected East Asian handmade papers. Full article

Slopes transitioning from open-pit to underground mining typically exhibit heterogeneous and nonlinear deformation characteristics. Under complex environmental disturbances, monitoring data are often affected by high noise and outliers, making it difficult to accurately capture critical deformation characteristics and posing challenges for landslide early warning and safety assessment. Therefore, it is necessary to develop a high-precision data optimization technique suitable for complex, high-noise monitoring time series data to improve slope stability evaluation and the robustness of prediction algorithms. Based on slope deformation monitoring data from the Hainan Shilu Iron Mine, the multi-type, nonlinear, and alternating acceleration-deceleration patterns of deformation time series data were analyzed, and the performances of multiple anomaly detection and interpolation compensation algorithms were compared. The results show that the Local Outlier Factor (LOF) and K-Nearest Neighbors (KNN) algorithms achieve better performance in processing noisy and dynamically varying time series data based on comparative evaluations of detection accuracy and interpolation error. Furthermore, a Bessel function-based denoising technique was proposed for landslide monitoring systems. This technique effectively filters high-frequency noise while preserving the main characteristics of the data and outperforms conventional methods, including the Moving Average Method (MAM), Triple Exponential Smoothing (TES), and Least Squares Method (LSM). The proposed technique, integrating LOF-based anomaly detection, KNN-based interpolation compensation, and Bessel function denoising, can effectively process slope deformation monitoring data characterized by multi-type, nonlinear, and alternating acceleration-deceleration patterns. Engineering application at the Hainan Shilu Iron Mine demonstrated that the proposed technique improves data quality and model prediction performance, providing valuable support for slope stability analysis and disaster early warning systems in slopes transitioning from open-pit to underground mining. Full article

Accurate prediction of PM_2.5 concentration is essential for public health and environmental protection, and specifically crucial for the management of the availability of sufficient health personnel during adverse health episodes. However, its nonlinearity, variability, and complexity make this task challenging. This study proposes a long short-term memory (LSTM) weighted by K-nearest neighbor (KNN) algorithm (namely Weighted KNN-LSTM Model) that can effectively predict the PM_2.5 concentration time series. Firstly, the K-nearest neighbors of each time point are sought based on the Euclidean distance within the data time range. Given that neighboring observations typically exert a more pronounced influence than distant ones in spatial processes, weights are accordingly assigned to these neighbors to quantitatively reflect their relative importance in the analysis. Subsequently, after the initial data is processed by the weighted KNN algorithm, it is reorganized and transformed into a reconstructed dataset with a size K times that of the original data. The data used for model training and the data used for evaluating the model’s prediction performance are completely independent, and the test dataset is never involved in the model training process to ensure the authenticity and reliability of the prediction performance evaluation. Then, the LSTM neural network model is trained on this new dataset to enhance its generalization ability. The experimental results show that the weighted KNN-LSTM model exhibits excellent predictive performance in predicting PM_2.5 concentration. It is important to note that the dataset used to evaluate the model’s performance was strictly independent from the data used to train the model. This separation ensures that the reported accuracy reflects true predictive capability rather than mere fitting quality. The model provides a technical reference for hourly PM_2.5 concentration prediction in Nanchang City, and the prediction results can be used as an auxiliary reference for regional air quality monitoring; the application of the model in heavy pollution warnings needs to be further optimized and verified by combining multi-source data such as meteorology, which provide reliable data support for the formulation of dynamic emission reduction policies. Full article