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Working with high-dimensional phylogenetic comparative data sets is challenging because likelihood-based multivariate methods suffer from low statistical performances as the number of traits p approaches the number of species n and because some computational complications occur when p exceeds n. Alternative phylogenetic comparative methods have recently been proposed to deal with the large p small n scenario but their use and performances are limited. Herein, we develop a penalized likelihood (PL) framework to deal with high-dimensional comparative data sets. We propose various penalizations and methods for selecting the intensity of the penalties. We apply this general framework to the estimation of parameters (the evolutionary trait covariance matrix and parameters of the evolutionary model) and model comparison for the high-dimensional multivariate Brownian motion (BM), Early-burst (EB), Ornstein-Uhlenbeck (OU), and Pagel’s lambda models. We show using simulations that our PL approach dramatically improves the estimation of evolutionary trait covariance matrices and model parameters when p approaches n, and allows for their accurate estimation when p equals or exceeds n. In addition, we show that PL models can be efficiently compared using generalized information criterion (GIC). We implement these methods, as well as the related estimation of ancestral states and the computation of phylogenetic principal component analysis in the R package RPANDA and mvMORPH. Finally, we illustrate the utility of the new proposed framework by evaluating evolutionary models fit, analyzing integration patterns, and reconstructing evolutionary trajectories for a high-dimensional 3D data set of brain shape in the New World monkeys. We find a clear support for an EB model suggesting an early diversification of brain morphology during the ecological radiation of the clade. PL offers an efficient way to deal with high-dimensional multivariate comparative data.

Monthly forecasting of streamflow is of particular importance in water resources management especially in the provision of rule curves for dams. In this paper, the performance of four data-driven models with different structures including Artificial Neural Network (ANN), Generalized Regression Neural Network (GRNN), Least Square-Support Vector Regression (LS-SVR), and K-Nearest Neighbor Regression (KNN) are evaluated in order to forecast monthly inflow to Karkheh dam, Iran, in linear and non-linear conditions while the optimized values of the model parameters are determined in the same condition via the Leave-One-Out Cross Validation (LOOCV) method. Results show that the performance of the models is different in linear and nonlinear conditions; the cumulative ranking of the models according to the three assessment criteria including NSE, RMSE and R2 indicates that ANN performs best in linear conditions while LS-SVR, GRNN and KNN are in the next ranks, respectively. But in nonlinear conditions, the best performance belongs to LS-SVR, followed by KNN, ANN, and GRNN models.

Crack propagation in solder joints remains a major challenge impacting the thermo-mechanical reliability of electronic devices, underscoring the importance of optimizing package and solder pad designs. Traditional Finite Element Analysis (FEA) techniques for predicting solder joint lifespan often rely on manual post-processing to identify high-risk regions for plastic strain accumulation. However, this manual process can fail to detect complex and subtle failure mechanisms and purely based on averaging the creep strain and correlating it to lifetime values collected from experiments using Coffin Manson equation. To address these limitations, this study presents an Artificial Intelligence (AI) framework designed for automated 3D FEA post-processing of surface-mounted devices (SMDs) assembled to Printed Circuit Board (PCB). This framework integrates 3D Convolutional Neural Networks (CNNs) and PointNet architectures to automatically extract complex spatial features from 3D FEA data. These learned features are then linked to experimentally measured solder joint lifetimes through fully connected neural network layers, allowing the model to capture complex and nonlinear failure behaviours. The research specifically targets crack development in solder joints of ceramic-based high-power LED packages used in automotive lighting systems. This dataset included variations in two-pad and three-pad configurations, as well as thin and thick film metallized ceramic substrates. Results from the study demonstrate that the PointNet model outperforms the 3D CNN, achieving a high correlation with experimental data (R 2 = 99.91%). This AI-driven, automated feature extraction approach significantly improves the accuracy and provide the more reliable models for solder joint lifetime predictions, offering a substantial improvement over traditional method.

Understanding the spatial variability of precipitation is essential for water resource management and climate adaptation, especially in arid and semi-arid regions with strong spatiotemporal heterogeneity. Traditional geostatistical methods, such as ordinary kriging, often struggle to capture nonlinear relationships between rainfall and spatial coordinates. This study focuses on comparing ML–RK methods for spatial interpolation using only latitude and longitude as predictors, rather than developing a full rainfall prediction model. As, machine learning techniques integrated with regression kriging (RK) have wide applications for capturing complex spatial patterns. Therefore, this study evaluates RK combined with six regression models including Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Neural Network (NN), Elastic Net (EN), and Polynomial Regression (PR). In this research, we used monthly and decadal averages of precipitation from 42 meteorological stations (2001–2021) of Pakistan. For assessing optimal spatial structure, four theoretical variogram models including exponential, circular, spherical, and linear–were tested using Leave-One-Out Cross-Validation. Here, the performance of the variogram was assessed using RMSE and MAE. Outcomes associated with this research show that RF-RK consistently outperformed other combinations of ML-RK. Consequently, the combination of ensemble learning and geostatistical interpolation effectively captured both nonlinear relationships and spatial dependencies. The resulting high-resolution rainfall maps can support climate adaptation planning, irrigation scheduling, and sustainable management of water resources in data-scarce regions such as Pakistan.

The advancement of deep learning in biometric systems, in which face and hand modalities have been widely implemented, leads to significant improvements in terms of speed performance and data confidentiality. Palmprint recognition is the main focus of the proposed approach, which deals with databases that are relatively smaller than other biometric datasets. A large and complex deep learning models may overfit and lose their ability to generalize when applied to such data. This study addresses this challenge by implementing a deep learning model suitable for palmprints, which are characterized by diversity and limited data. Initially, the appropriate Region of Interest (ROI) is extracted using active segmentation, which is fitting for dealing with the difficulty of obtaining palmprints from hand images with closely spaced or connected fingers. In the second stage, a novel customized LOOCV Leave-One-Out Cross Validation (A Modified-LOOCV) technique is integrated with a Siamese deep learning network for palmprint verification. Unlike conventional LOOCV, our modified scheme optimizes the computational cost while achieving a balanced evaluation on three different datasets. The proposed framework rivals the effectiveness of the advanced palmprint recognition systems with a high recognition accuracy of 99.75%, improved equal error rates (EER), reduced to 0.002, and faster matching time, making it highly suitable for field application. [JJCIT 2025; 11(4.000): 499-516]

Globally, coal remains one of the natural resources that provide power to the world. Thousands of people are involved in coal collection, processing, and transportation. Particulate coal dust is produced during these processes, which can crush the lung structure of workers and cause pneumoconiosis. There is no automated system for detecting and monitoring diseases in coal miners, except for specialist radiologists. This paper proposes ensemble learning techniques for detecting pneumoconiosis disease in chest X-ray radiographs (CXRs) using multiple deep learning models. Three ensemble learning techniques (simple averaging, multi-weighted averaging, and majority voting (MVOT)) were proposed to investigate performances using randomised cross-folds and leave-one-out cross-validations datasets. Five statistical measurements were used to compare the outcomes of the three investigations on the proposed integrated approach with state-of-the-art approaches from the literature for the same dataset. In the second investigation, the statistical combination was marginally enhanced in the ensemble of multi-weighted averaging on a robust model, CheXNet. However, in the third investigation, the same model elevated accuracies from 87.80 to 90.2%. The investigated results helped us identify a robust deep learning model and ensemble framework that outperformed others, achieving an accuracy of 91.50% in the automated detection of pneumoconiosis.

Cell-mediated immunity and chronic inflammation are hallmarks of chronic kidney disease (CKD). Growth differentiation factor 15 (GDF15) is a marker of inflammation and an integrative signal in stress conditions. Epidermal growth factor (EGF) is a tubule-specific protein that modulates the regeneration of injured renal tubules. Neopterin is a product of activated monocytes and macrophages and serves as a marker of cell-mediated immunity. Our aim was to assess the role of the above-mentioned parameters in the progression of CKD in children using artificial intelligence tools. The study group consisted of 151 children with CKD stages 1–5. EGF, GDF15, and neopterin serum concentrations were assessed by ELISA. The patients’ anthropometric data, biochemical parameters, EGF, GDF15, and neopterin serum values were implemented into the artificial neural network (ANN). The most precise model contained EGF, GDF15, and neopterin as input parameters and classified patients into either CKD 1–3 or CKD 4–5 groups with an excellent accuracy of 96.77%. The presented AI model, with serum concentrations of EGF, GDF15, and neopterin as input parameters, may serve as a useful predictor of CKD progression. It suggests the essential role of inflammatory processes in the renal function decline in the course of CKD in children.

Background Endometrial cancer (EC) is a common gynecologic malignancy with rising incidence and significant molecular heterogeneity. This study aimed to develop an integrated prognostic model using pathomics features derived from histopathological images. Methods We retrospectively analyzed hematoxylin and eosin-stained whole slide images and clinical data from 511 EC patients in the TCGA database. Pathomics features were extracted using the same methodology as the reference study. Patients were randomly divided into training ( n  = 341) and validation ( n  = 170) cohorts at a 2:1 ratio. Under a leave-one-out cross-validation framework, features were selected using LASSO combined with random survival forest to construct a pathomics score. Differential gene expression and functional enrichment were analyzed and a nomogram integrating the pathomics score with clinical variables was developed and evaluated. Results The pathomics model demonstrated excellent prognostic prediction, with AUCs of 0.966, 0.724, and 0.918 in the training, validation, and whole cohorts for 5-year survival, respectively. The pathomics score showed significant associations with FIGO stage, grade, lymph node metastasis, and recurrence ( p  < 0.05). Differential gene expression analysis revealed enrichment in EC-related pathways, MAPK signaling, estrogen signaling, and HIF-1 signaling pathways. Multivariable analysis confirmed FIGO stage, grade, lymph node metastasis, and pathomics score as independent prognostic factors. The nomogram incorporating these factors showed significantly improved in overall survival (all p  < 0.001 in the 3 cohorts) and predictive evaluation of AUCs (increases of 0.111, 0.132, and 0.118, respectively) with good calibration. Conclusion The proposed nomogram integrating pathomics and clinical factors provides accurate prognostic prediction for EC patients, offering a valuable tool for risk stratification and personalized management.

Wine phenolics serve as robust chemical signatures correlated to grape variety, processing, and regional identity. This study explores the potential of machine learning algorithms, combined with the phenolic profiles of Albanian wines, to classify them according to grape variety. Geographic origin analysis was conducted as a preliminary exploration. The dataset of phenolic compounds included white and red wines, spanning the 2017 to 2021 vintages. Using five supervised algorithms—Support Vector Machine (SVM), Random Forest, XGBoost, Logistic Regression, and K-Nearest Neighbors—a high classification accuracy was achieved, with SVM reaching 100% under Leave-One-Out Cross-Validation (LOOCV). To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) and stratified cross-validation were applied. Random Forest feature importance consistently highlighted trans-Fertaric acid and Procyanidin B3 as dominant discriminants. Parallel coordinates plots demonstrated clear varietal patterns driven by phenolic differences, while PCA and hierarchical clustering confirmed unsupervised grouping consistent with wine type and maceration level. Permutation testing (1000 iterations) confirmed the non-randomness of model performance. These findings show that a small set of phenolic markers can offer high classification accuracy, supporting chemically based wine authentication. Although the dataset is relatively small, thorough cross-validation, non-redundant modeling, and chemical interpretability provide a solid foundation for scalable methods. Future work will expand the dataset and explore sensor-based phenolic measurement to enable rapid authentication in wine.

Purpose. The selection of an appropriate variographic model is crucial in geostatistics to obtain accurate estimates of mineral reserves. The aim of this work is to develop a reserve estimation tool using a geostatistical approach. Methodology. The geostatistical approach is based on selecting the most representative variographic models for the studied variables. The model selection is done by applying a cross-validation procedure leave-one-out (LOOCV). LOOCV is a resampling technique used in statistical analysis and machine learning to estimate the generalization error of a model and compare the performance of different models. The studied variables are then estimated using ordinary kriging. Findings. The application of the proposed approach has resulted in satisfactory results in terms of dispersion of grades and thicknesses of mineralized layers in a phosphate deposit. To evaluate the quality of the adjustment models obtained, efficiency factors such as Nash-Sutcliffe, and RMSE (Root Mean Square Error), were employed. These factors provide quantitative measures of the agreement between the observed and predicted values. The NSE (Nash-Sutcliffe efficiency) and RMSE (root mean square error) values of 0.572 and 6.599, respectively, indicate a better fit and greater accuracy of the adjustment models. The accuracy and efficiency criteria of the studied variables have acceptable values, with a mean square error (MSE) of 1.54 · 10-7. Originality. The combination of the least squares and LOOCV methods in the geostatistical analysis leads to improved estimation precision, greater reliability in representing the spatial variability of the parameters, and enhanced confidence in the validity of the adjustment models. Practical value. The development of a computer code for this geostatistical approach provides a practical tool for decision-makers to use in the management and exploitation of mining sites. Overall, this study has contributed to the advancement of geostatistical techniques and their application in the mining industry.