Search Results (2,980)

The Abelar pilot basin in Coruña (northwestern Spain) has been monitored for hydrological and hydrochemical data to assess the effects of eucalyptus plantation and manure applications on water resources, water quality, and nitrate contamination. Here, we report the machine learning analysis of hydrological and hydrochemical data from the Abelar basin. K-means cluster analysis (CA) is used to relate nitrate concentrations at the outlet of the basin with daily interflows and groundwater flows calculated with a hydrological balance. CA identifies three linearly separable clusters. Times series Gaussian process regression (TS-GPR) is employed to predict surface water nitrate concentration by incorporating hydrological variables as additional input parameters using a time series shifting. TS-GPR allows modelling nitrate concentrations based on shifted interflows and groundwater flows and chemical concentrations with R² = 0.82 and 0.80 for training and testing, respectively. Groundwater flow from five days prior to the current date, $Q_{g5}$, is the most important input parameter of the TS-GPR model. Interaction effects between the variables are found. TS-GPR validation with recent data provides results consistent with those of testing (R² = 0.85). Model inspection by permutation feature importance and partial dependence plots shows interactions between $Q_{g5}$ and Cl, and between Ca and Mg. Full article

This article discusses the design of a high-performance quasi-optical mode converter for the $T{E}_{33,12}$ mode at 210 GHz. The conversion process is challenging due to a caustic-to-cavity radius ratio of approximately 0.41. The mode converter employs an optimized dimpled wall launcher, analyzed using coupling mode theory with twenty-five coupled modes, compared to the usual nine modes and optimized reflector systems, to effectively address the conversion challenge.Electromagnetic field analysis within the launcher wall was optimized using MATLAB R2021b. The radiation fields from the launcher were analyzed in free space using Gaussian optics and vector diffraction theory. The mirror system consists of a quasi-elliptical mirror, an elliptical mirror, and phase-corrected parabolic mirrors. Following phase correction, the output window achieved a scalar Gaussian mode content of 99.0% and a vector Gaussian mode content of 97.4%. Full article

Policy space response oracles (PSRO) is an important algorithmic framework for approximating Nash equilibria in two-player zero-sum games. Enhancing policy diversity has been shown to improve the performance of PSRO in this approximation process significantly. However, existing diversity metrics are often prone to redundancy, which can hinder optimal strategy convergence. In this paper, we introduce the policy similarity measure (PSM), a novel approach that combines Gaussian and cosine similarity measures to assess policy similarity. We further incorporate the PSM into the PSRO framework as a regularization term, effectively fostering a more diverse policy population. We demonstrate the effectiveness of our method in two distinct game environments: a non-transitive mixture model and Leduc poker. The experimental results show that the PSM-augmented PSRO outperforms baseline methods in reducing exploitability by approximately 7% and exhibits greater policy diversity in visual analysis. Ablation studies further validate the benefits of combining Gaussian and cosine similarities in cultivating more diverse policy sets. This work provides a valuable method for measuring and improving the policy diversity in two-player zero-sum games. Full article

To address the limited data availability and low predictive accuracy of nickel matte grade models in the early stages of facility operation, this study introduces a unique stepwise prediction methodology that integrates data augmentation and ensemble learning, specifically tailored for limited industrial datasets. Predicting matte nickel grade accurately is critical for nickel sulfate production, a key precursor in cathode manufacturing. However, in newly adopted facilities, operational data are scarce, posing a major challenge for conventional machine learning models that require large, well-balanced datasets to generalize effectively. Moreover, the nonlinear dependencies between raw material composition, operational conditions, and metallurgical reactions further complicate the prediction task, often leading to high errors in traditional regression models. To overcome these challenges, this study introduces an innovative approach that integrates feature engineering, Gaussian noise augmentation, SMOTE regression, and a stacking ensemble model, using XGBoost (2.0.3) and CatBoost (1.2.7). First, input variables were refined through feature engineering, followed by data augmentation to enhance dataset diversity and improve model robustness. Next, a stacking ensemble framework was implemented to mitigate overfitting and enhance predictive accuracy. Finally, SHAP, an XAI technique that quantifies the impact of each input variable on the model’s predictions based on cooperative game theory, was employed to interpret key process variables, offering deeper insights into the factors influencing nickel grade. The experimental results demonstrate a substantial improvement in prediction accuracy, with the R² coefficient increasing from 0.3050 to 0.9245, alongside significant reductions in RMSE, MAE, and MAPE. The proposed methodology not only enhances predictive performance in data-scarce industrial environments but also provides an interpretable framework for real-world process optimization. These findings validate its applicability to nickel matte operations, offering a scalable and explainable machine learning approach for metallurgical industries with limited data availability. Full article

With the advent of cloud computing and the era of big data, there is an increasing focus on privacy computing. Consequently, homomorphic encryption, being a primary technique for achieving privacy computing, is held in high regard. Nevertheless, the efficiency of homomorphic encryption schemes is significantly impacted by bootstrapping. Enhancing the efficiency of bootstrapping necessitates a dual focus: reducing the computational burden of outer product operations integral to the process while rigorously constraining the noise generated by bootstrapping within predefined threshold limits. The FINAL scheme is a fully homomorphic encryption scheme based on the number theory research unit (NTRU) and learning with errors (LWE) assumptions. The performance of the FINAL scheme is better than that of the TFHE scheme, with faster bootstrapping and smaller bootstrapping and key-switching keys. In this paper, we introduce ellipsoidal Gaussian sampling to generate keys $\mathit{f}$ and $\mathit{g}$ in the bootstrapping of the FINAL scheme, so that the standard deviations of keys $\mathit{f}$ and $\mathit{g}$ are different and reduce the bootstrapping noise by 76%. However, when q is fixed, the boundary for bootstrapping noise remains constant. As a result, larger decomposition bases are used in bootstrapping to reduce the total number of polynomial multiplications by 47%, thus improving the efficiency of the FINAL scheme. The optimization scheme outperforms the original FINAL scheme with 33.3% faster bootstrapping, and the memory overhead of blind rotation keys is optimized by 47%. Full article

A novel partially functional linear regression model with random effects is proposed to address the case of Euclidean covariates and functional covariates. Specifically, the model assumes that the random effects follow a Gaussian process prior to establish the linkage structure between Euclidean covariates and scalar responses. For functional covariates, a linear relationship with scalar responses is assumed, and the functional covariates are approximated using the Karhunen–Loève expansion. To enhance the robustness of the predictive model, a cross-validation-based ensemble strategy is employed to optimize the proposed method. Results from both simulation studies and real-world data analyses demonstrate the superior performance and competitiveness of the proposed approach in terms of prediction accuracy and model stability. Full article

The inverse Gaussian (IG) distribution exhibits asymmetry and right skewness. This distribution presents values uniformly, encompassing wait length, stochastic processes, and rates of accident occurrences. The delta-inverse Gaussian (delta-IG) distribution is suitable for modeling traffic accident data as a mortality count, especially in cases when accidents may not occur. The confidence interval (CI) for the variance and standard deviation of the delta-IG distribution for the accident count is crucial for evaluating risk, allocating resources, and formulating enhancement protocols for transportation safety. We aim to construct confidence intervals for variance and standard deviation in the delta-IG population using several approaches: Adjusted GCI (AGCI), Parametric Bootstrap Percentile CI (PBPCI), fiducial CI (FCI), and Bayesian credible interval (BCI). The AGCI, PBPCI, and FCI will be utilized with estimation methods for proportions which are VST, Wilson’s score, and Hannig approaches. Monte Carlo simulations were evaluated, and the suggested confidence interval approach was employed for the average width (AW) and coverage probability (CP). The findings demonstrated that the AGCI based on the VST method employed successful approaches, as seen in their CP and AW. We employed these approaches to produce CIs for the variance and S.D. of the mortality count in Bangkok. Full article

To support the sustainable use of marine resources, regulations have been proposed to reduce fish discards focusing on the registration of all listed species. To comply with such regulations, computer vision methods have been developed. Nevertheless, current approaches are constrained by their closed-set nature, where they are designed only to recognize fish species that were present during training. In the real world, however, samples of unknown fish species may appear in different fishing regions or seasons, requiring fish classification to be treated as an open-set problem. This work focuses on the assessment of open-set recognition to automate the registration process of fish. The state-of-the-art Multiple Gaussian Prototype Learning (MGPL) was compared with the simple yet powerful Open-Set Nearest Neighbor (OSNN) and the Probability of Inclusion Support Vector Machine (PISVM). For the experiments, the Fish Detection and Weight Estimation dataset, containing images of 2216 fish instances from nine species, was used. Experimental results demonstrated that OSNN and PISVM outperformed MGPL in both recognizing known and unknown species. OSNN achieved the best results when classifying samples as either one of the known species or as an unknown species with an F1-macro of $0.79\pm 0.05$ and an AUROC score of $0.92\pm 0.01$ surpassing PISVM by $0.05$ and $0.03$, respectively. Full article

Estimating the state of health of lithium-ion batteries in energy storage systems is a key step in their subsequent safety monitoring and energy optimization management. This study proposes a method for estimating the state of health of lithium-ion batteries based on feature reconstruction and Transformer-GRU parallel architecture to solve the problems of noisy feature data and the poor applicability of a single model to different types and operating conditions of batteries. First, the incremental capacity curve was constructed based on the charging data, smoothed using Gaussian filtering, and the diverse health features were extracted in combination with the charging voltage curve. Then, this study used the CEEMDAN algorithm to reconstruct the IC curve features, which reduces noisy data due to the process of data collection and processing. Lastly, this study used the cross-attention mechanism to fuse the Transformer and GRU neural networks, which constructed a Transformer-GRU parallel model to improve its ability to mine time-dependent features and global features for state of health estimation. This study conducted experiments using three datasets from Oxford, CALCE, and NASA. The results show that the RMSE of the state of health estimation by the proposed method is 0.0071, which is an improvement of 61.41% in the accuracy of its baseline model. Full article

To achieve intelligent manufacturing and improve the machining quality of machine tools, this paper proposes an interpretable machining size prediction model combining eXtreme Gradient Boosting (XGBoost), autoencoder (AE), and Shapley additive explanation (SHAP) analysis. In this study, XGBoost was used to establish an evaluation system for the actual machining size of computer numerical control (CNC) machine tools. The XGBoost model was combined with SHAP approximation to effectively capture local and global features in the data using autoencoders and transform the preprocessed data into more representative feature vectors. Grey correlation analysis (GRA) and principal component analysis (PCA) were used to reduce the dimensions of the original data features, and the synthetic minority overstimulation technique of the Gaussian noise regression (SMOGN) method was used to deal with the problem of data imbalance. Taking the actual size of the machine tool as the response parameter, based on the size parameters in the milling process of the CNC machine tool, the effectiveness of the model is verified. The experimental results show that the proposed AE-XGBoost model is superior to the traditional XGBoost method, and the prediction accuracy of the model is 7.11% higher than that of the traditional method. The subsequent SHAP analysis reveals the importance and interrelationship of features and provides a reliable decision support system for machine tool processing personnel, helping to improve processing quality and achieve intelligent manufacturing. Full article

Total electron content (TEC) serves as a key parameter characterizing ionospheric conditions. Accurate prediction of TEC plays a crucial role in improving the precision of Global Navigation Satellite Systems (GNSS). However, existing research have predominantly emphasized spatial variations in the ionosphere, neglecting the periodic changes of the ionosphere with the diurnal cycle. In this paper, we propose a TEC prediction model, which simultaneously considers both spatial and temporal characteristics to extract spatiotemporal features of ionospheric distribution. Additionally, we integrate several space weather element datasets into the prediction model framework, allowing the generation of multiple space weather feature values that represent the influence of space weather on the ionosphere at different latitudes and longitudes. Moreover, we apply Gaussian process regression (GPR) interpolation to geomagnetic data to characterize impact on the ionosphere, thereby enhancing the prediction accuracy. We compared our model with traditional image-based models such as convolutional neural networks (CNNs), convolutional long short-term memory networks (ConvLSTMs), a self-attention mechanism-integrated ConvLSTM (SAM-ConvLSTM) model, and one-day predicted ionospheric products (C1PG) provided by the Center for Orbit Determination in Europe (CODE). We also examined the effect of using different numbers of space weather feature values in these models. Our model outperforms the comparison models in terms of prediction error metrics, including mean absolute error (MAE), root mean square error (RMSE), correlation coefficient (CC), and the structural similarity index (SSIM). Furthermore, we analyzed the influence of different batch sizes on model training accuracy to find the best performance of each model. In addition, we investigated the model performance during geomagnetic quiet periods, where our model provided the most accurate predictions and demonstrates higher prediction accuracy in the equatorial anomaly region. We also analyzed the prediction performance of all models during space weather events. The results indicate that the proposed model is the least affected during geomagnetic storms and demonstrates superior prediction performance compared to other models. This study presents a more stable and high-performance spatiotemporal prediction model for TEC. Full article

Inertial navigation systems augmented with visual and wheel odometry measurements have emerged as a robust solution to address uncertainties in robot localization and odometry. This paper introduces a novel data-driven approach to compensate for wheel slippage in visual-inertial-wheel odometry (VIWO). The proposed method leverages Gaussian process regression (GPR) with deep kernel design and long short-term memory (LSTM) layers to model and mitigate slippage-induced errors effectively. Furthermore, a feature confidence estimator is incorporated to address the impact of dynamic feature points on visual measurements, ensuring reliable data integration. By refining these measurements, the system utilizes a multi-state constraint Kalman filter (MSCKF) to achieve accurate state estimation and enhanced navigation performance. The effectiveness of the proposed approach is demonstrated through extensive simulations and experimental validations using real-world datasets. The results highlight the ability of the method to handle challenging terrains and dynamic environments by compensating for wheel slippage and mitigating the influence of dynamic objects. Compared to conventional VIWO systems, the integration of GPR and LSTM layers significantly improves localization accuracy and robustness. This work paves the way for deploying VIWO systems in diverse and unpredictable environments, contributing to advancements in autonomous navigation and multi-sensor fusion technologies. Full article

The paper presents a fast method for 3D point cloud target extraction, addressing the challenge of time-consuming processing in LiDAR-based 3D point cloud data. The method begins with the acquisition of environmental 3D point cloud data using LiDAR, which is then projected onto a 2D cylindrical map. We propose a method for rapid target extraction from LiDAR-based 3D point cloud data, which includes key steps such as projection into 2D space, image processing for segmentation, and target extraction. A mapping matrix between the 2D grayscale image and the cylindrical projection is derived through Gaussian elimination. A target backtracking search algorithm is used to map the extracted target region back to the original 3D point cloud, enabling precise extraction of the 3D target points. Near-field experiments using hybrid solid-state LiDAR demonstrate the method’s effectiveness, requiring only 0.53 s to extract 3D target point clouds from datasets containing hundreds of thousands of points. Further, far-field rocket launch experiments show that the method can extract target point clouds within 158 milliseconds, with measured positional offsets of 0.2159 m and 0.1911 m as the rocket moves away from the launch tower. Full article

Event–event causal relation extraction (ECRE) represents a critical yet challenging task in natural language processing. Existing studies primarily focus on extracting causal sentences and events, despite the use of joint extraction methods for both tasks. However, both pipeline methods and joint extraction approaches often overlook the impact of document-level event temporal sequences on causal relations. To address this limitation, we propose a model that incorporates document-level event temporal order information to enhance the extraction of implicit causal relations between events. The proposed model comprises two channels: an event–event causal relation extraction channel (ECC) and an event–event temporal relation extraction channel (ETC). Temporal features provide critical support for modeling node weights in the causal graph, thereby improving the accuracy of causal reasoning. An Association Link Network (ALN) is introduced to construct an Event Causality Graph (ECG), incorporating an innovative design that computes node weights using Kullback–Leibler divergence and Gaussian kernels. The experimental results indicate that our model significantly outperforms baseline models in terms of accuracy and weighted average F1 scores. Full article

This study analyzed the spatiotemporal changes in grassland NDVI from 2000 to 2020 in the eastern route of the China–Mongolia–Russia Economic Corridor, a region with frequent ecological–economic interactions, and explained the main driving factors, influencing patterns, and degrees of grassland NDVI changes in different regions. Based on MODIS NDVI data, the study employs emerging spatiotemporal hotspot analysis, Maximum Relevance Minimum Redundancy (mRMR) feature selection, and Gaussian Process Regression (GPR) to reveal the spatiotemporal variation characteristics of grassland NDVI, while identifying long-term stable trends, and to select the most relevant and non-redundant factors to analyze the main driving factors of grassland NDVI change. Partial dependence plots were used to visualize the response and sensitivity of grassland NDVI to various factors. The results show the following: (1) From 2000 to 2020, the NDVI of grassland in the study area showed an overall upward trend, from 0.61 to 0.65, with significant improvement observed in northeastern China and northeastern Russia. (2) Spatiotemporal hotspot analysis indicates that 51% of the area is classified as persistent hotspots for grassland NDVI, mainly distributed in Russia, whereas 12% of the area is identified as persistent cold spots, predominantly located in Mongolia. (3) The analysis of key drivers reveals that precipitation and land surface temperature are the dominant climatic factors shaping grassland NDVI trends, while the effects of soil conditions and human activity vary regionally. In China, NDVI is primarily driven by land surface temperature (LST), GDP, and population density; in Mongolia, precipitation, LST, and GDP exert the strongest influence; whereas in Russia, livestock density and soil organic carbon play the most significant roles. (4) For the whole study area, in persistent cold spot areas of grassland NDVI, the negative effects of rising land surface temperature were most pronounced, reducing NDVI by 36% in the 25–40 °C range. The positive effects of precipitation on NDVI were most evident under low to moderate precipitation conditions, with the effects diminishing as precipitation increased. Soil moisture and soil pH have stronger effects in persistent hotspot areas. Regarding human activity factors, the livestock factor in Mongolia shows an inverted U-shaped relationship with NDVI, and increasing population density contributed to grassland degradation in persistent cold spots. Proper grazing intensity regulation strategy is crucial in these areas with inappropriate grazing intensity, while social and economic activities promoted vegetation cover improvement in persistent hotspots in China and Russia. These findings provide practical insights to guide grassland ecosystem restoration and ensure sustainable development along the eastern route of the China–Mongolia–Russia Economic Corridor. China should prioritize ecological compensation policies. Mongolia needs to integrate traditional nomadic grazing with modern practices. Russia should focus on strengthening regulatory frameworks to prevent the over-exploitation of grasslands. Especially for persistent cold spot areas of grassland NDVI in Mongolia and Russia that are prone to grassland degradation, attention should be paid to the significant negative impact of livestock on grassland. Full article