Search Results (12,194)

The availability of reliable and spatially distributed rainfall data is a key element flood and landslide risk assessment, both for forecasting and post-event analysis. In this context, this study evaluates the contribution of radar-based rainfall estimates to enhancing the spatial accuracy of precipitation fields with respect to those derived from rain gauge networks alone. The analysis was conducted over a ~100 km² area in the Liguria Region, north-western Italy, characterized by a dense rain gauge network, with an average density of one gauge per 10 km², and covers seven years of hourly rainfall observations. Radar-derived rainfall fields, available at a 1 × 1 km² spatial resolution, were locally corrected across the study area by interpolating gauge-based local correction factors through an Inverse Distance Weighting (IDW) scheme. The corrected radar fields were then assessed through Leave-P-Out Cross-Validation and rainfall-intensity-based classification, also simulating scenarios with progressively reduced gauge density. The results demonstrate that radar-corrected estimates systematically provide a more accurate spatial representation of rainfall, especially for high-intensity events and in capturing the actual magnitude of local rainfall peaks, even in areas covered by a dense rain gauge network. Regarding the implications for rainfall-induced landslide hazard assessment, the analysis of 56 landslides from the ITALICA (Italian Rainfall-Induced Landslides Catalogue) database showed that including radar information can lead to significant differences in the estimation of rainfall thresholds for landslide initiation compared with gauge-only data. Full article

The decoupling properties and low-inertia characteristics of large-scale wind power have heightened concerns regarding power grid frequency stability, particularly as modern power systems impose stringent frequency regulation requirements on wind integration, leading to an increased complexity of frequency response characteristics under fault conditions. To address this challenge in high-wind-penetration grids, this paper proposes a post-fault frequency dynamics analysis method capable of concurrently accommodating multi-wind-speed scenarios through three key innovations: the linearization of traditional AC system components (including network equations, composite load models, and generator prime mover-governor systems) to establish nodal power increment equations; the development of wind turbine frequency regulation models under diverse wind conditions using small-signal analysis, incorporating regional operational disparities and refined by information entropy-based reliability quantification for adaptive parameter adjustment; and the derivation of the system state equation for post-fault frequency response using wide-area measurement system (WAMS) data, yielding an analytical model that captures region-specific regulation characteristic disparities for physically faithful frequency analysis. Validation via tailored IEEE 39-node simulations convincingly demonstrates the method’s effectiveness and superiority in handling fault-induced transients and wind variability. Full article

Reliable and efficient detection of persimmons provides the foundation for precise maturity evaluation. Persimmon ripeness detection remains challenging due to small target sizes, frequent occlusion by foliage, and motion- or focus-induced blur that degrades edge information. This study proposes YOLOv11-4ConvNeXtV2, an enhanced detection framework that integrates a ConvNeXtV2 backbone with Fully Convolutional Masked Auto-Encoder (FCMAE) pretraining, Global Response Normalization (GRN), and Single-Head Self-Attention (SHSA) mechanisms. We present a comprehensive persimmon dataset featuring sub-block segmentation that preserves local structural integrity while expanding dataset diversity. The model was trained on 4921 annotated images (original 703 + 6 × 703 augmented) collected under diverse orchard conditions and optimized for 300 epochs using the Adam optimizer with early stopping. Comprehensive experiments demonstrate that YOLOv11-4ConvNeXtV2 achieves 95.9% precision and 83.7% recall, with mAP@0.5 of 88.4% and mAP@0.5:0.95 of 74.8%, outperforming state-of-the-art YOLO variants (YOLOv5n, YOLOv8n, YOLOv9t, YOLOv10n, YOLOv11n, YOLOv12n) by 3.8–6.3 percentage points in mAP@0.5:0.95. The model demonstrates superior robustness to blur, occlusion, and varying illumination conditions, making it suitable for deployment in challenging maturity detection environments. Full article

There is an urgent need for an updated and relevant soil information system (SIS) to sustainably use and manage the land across Africa. Accurate data on soil pollution is essential for effective decision-making in soil health monitoring and management. Unfortunately, the data and information are not usually presented in formats that can easily guide decision-making. The objectives of this work were to (i) assess the availability of soil pollution maps, (ii) evaluate the methodologies used in creating these maps, (iii) explore the role of soil pollution maps in soil health monitoring, and (iv) identify gaps and challenges in soil pollution mapping in Africa. Soil pollution maps across Africa are created on a local scale, with highly variable sampling size and low sampling density. The most used mapping techniques include spatial interpolation (kriging and inverse distance weighting). Among the types of soil pollutants mapped, heavy metals have received priority, while pesticides and persistent organic pollutants have received less attention. Soil pollution mapping is not incorporated within the SIS framework due to lack of reliable spatially comprehensive data and technological and institutional barriers. Current efforts remain fragmented, site-specific, and methodologically inconsistent, resulting in significant data gaps that hinder reliable monitoring and limit progress in soil pollution mapping. Full article

Modern healthcare and engineering both rely on robust reliability models, where handling censored data effectively translates into longer-lasting devices, improved therapies, and safer environments for society. To address this, we develop a novel inferential framework for the ZLindley (ZL) distribution under the improved adaptive progressive Type-II censoring strategy. The proposed approach unifies the flexibility of the ZL model—capable of representing monotonically increasing hazards—with the efficiency of an adaptive censoring strategy that guarantees experiment termination within pre-specified limits. Both classical and Bayesian methodologies are investigated: Maximum likelihood and log-transformed likelihood estimators are derived alongside their asymptotic confidence intervals, while Bayesian estimation is conducted via gamma priors and Markov chain Monte Carlo methods, yielding Bayes point estimates, credible intervals, and highest posterior density regions. Extensive Monte Carlo simulations are employed to evaluate estimator performance in terms of bias, efficiency, coverage probability, and interval length across diverse censoring designs. Results demonstrate the superiority of Bayesian inference, particularly under informative priors, and highlight the robustness of HPD intervals over traditional asymptotic approaches. To emphasize practical utility, the methodology is applied to real-world reliability datasets from clinical trials on leukemia patients and hydrological measurements from River Styx floods, demonstrating the model’s ability to capture heterogeneity, over-dispersion, and increasing risk profiles. The empirical investigations reveal that the ZLindley distribution consistently provides a better fit than well-known competitors—including Lindley, Weibull, and Gamma models—when applied to real-world case studies from clinical leukemia trials and hydrological systems, highlighting its unmatched flexibility, robustness, and predictive utility for practical reliability modeling. Full article

Today, a number of works are being carried out all over the world to develop data processing and management systems, as well as to apply artificial intelligence and information technologies in the fields of production, science, education, and healthcare. The optimization of the management of socio-economic process systems, and the management and reliability of databases of the digital payment information-based information systems of enterprises and organizations are relevant. This research work investigates the issue of increasing the reliability of information in information systems working with payment information. The characteristics of ambiguous suspicious transactions in payment systems are analyzed, and based on the analysis, preliminary data preparation stages are carried out for the intelligent detection of ambiguous suspicious transactions. Traditional and neural network models of machine learning for the detection of suspicious transactions in payment information management systems are developed, and a comparative analysis is carried out. Furthermore, to enhance the performance of the core LSTM model, an Artificial Bee Colony (ABC) optimization algorithm was integrated for automated hyperparameter tuning, which improved the model’s accuracy and efficiency in identifying complex fraudulent patterns. Full article

Protein–DNA and protein–RNA interactions are central to gene regulation and genetic disease, yet experimental identification remains costly and complex. Machine learning (ML) offers an efficient alternative, though challenges persist in representing protein sequences due to residue variability, dimensionality issues, and the risk of losing biological context. Traditional approaches such as k-mer counting or neural network encodings provide standardized sequence representations but often demand high computational resources and may obscure functional information. To address these limitations, a novel encoding method based on interpolation of physicochemical properties (PCPs) is introduced. Discrete PCPs values are transformed into continuous functions using logarithmic enhancement, highlighting residues that contribute most to nucleic acid interactions while preserving biological relevance across variable sequence lengths. Statistical features extracted from the resulting spectra via Tsfresh are then used for binary classification of DNA- and RNA-binding proteins. Six classifiers were evaluated, and the proposed method achieved up to 99% accuracy, precision, recall, and F1 score when amino acid highlighting was applied, compared with 66% without highlighting. Benchmarking against k-mer and neural network approaches confirmed superior efficiency and reliability, underscoring the potential of this method for protein interaction prediction. Our framework may be extended to multiclass problems and applied to the study of protein variants, offering a scalable tool for broader protein interaction prediction. Full article

Exposure to degraded indoor air quality (IAQ) conditions represents a major concern for health and well-being. PM_2.5 is among the most prevalent indoor air pollutants and constitutes a key indicator in IAQ assessment. Conventional IAQ frameworks often neglect personalization, which in turn compromises the reliability of exposure estimation and the interpretation of associated health implications. In response to this limitation, the present study introduces a human-centric framework that couples wearable sensing with deep learning, employing a low-cost wearable device to capture PM_2.5 concentrations in the immediate human vicinity and an attention-based Long-Short Term Memory (LSTM) to deliver 5-min-ahead exposure predictions. During evaluation, the proposed framework demonstrated strong and consistent performance across both stable conditions and transient spikes in PM_2.5, yielding a Mean Absolute Error (MAE) of 0.181 µg/m³. These findings highlighted the synergistic potential between wearable sensing and data-driven modeling in advancing personalized IAQ forecasting, informing proactive IAQ management strategies, and ultimately promoting healthier built environments. Full article

Accurate pose estimation of non-cooperative space objects is crucial for applications such as satellite maintenance, space debris removal, and on-orbit assembly. However, monocular pose estimation methods face significant challenges in environments with limited visibility. Different from the traditional pose estimation methods that use images from a single band as input, we propose a novel deep learning-based pose estimation framework for non-cooperative space objects by fusing visible and infrared images. First, we introduce an image fusion subnetwork that integrates multi-scale features from visible and infrared images into a unified embedding space, preserving the detailed features of visible images and the intensity information of infrared images. Subsequently, we design a robust pose estimation subnetwork that leverages the rich information from the fused images to achieve accurate pose estimation. By combining these two subnetworks, we construct the Visible and Infrared Fused Pose Estimation Framework (VIPE) for non-cooperative space objects. Additionally, we present a Bimodal-Vision Pose Estimation (BVPE) dataset, comprising 3,630 visible-infrared image pairs, to facilitate research in this domain. Extensive experiments on the BVPE dataset demonstrate that VIPE significantly outperforms existing monocular pose estimation methods, particularly in complex space environments, providing more reliable and accurate pose estimation results. Full article

Hyperspectral unmixing aims to extract pure spectral signatures (endmembers) and estimate their corresponding abundance fractions from mixed pixels, enabling quantitative analysis of surface material composition. However, in geological mineral exploration, existing unmixing methods often fail to explicitly identify informative spectral bands, lack inter-layer information transfer mechanisms, and overlook the physical constraints intrinsic to the unmixing process. These issues result in limited directionality, sparsity, and interpretability. To address these limitations, this paper proposes a novel model, CResDAE, based on a deep autoencoder architecture. The encoder integrates a channel attention mechanism and deep residual modules to enhance its ability to assign adaptive weights to spectral bands in geological hyperspectral unmixing tasks. The model is evaluated by comparing its performance with traditional and deep learning-based unmixing methods on synthetic datasets, and through a comparative analysis with a nonlinear autoencoder on the Urban hyperspectral scene. Experimental results show that CResDAE consistently outperforms both conventional and deep learning counterparts. Finally, CResDAE is applied to GF-5 hyperspectral imagery from Yunnan Province, China, where it effectively distinguishes surface materials such as Forest, Grassland, Silicate, Carbonate, and Sulfate, offering reliable data support for geological surveys and mineral exploration in covered regions. Full article

Despite the widespread success of convolutional deep learning frameworks in computer vision, significant limitations persist in medical image analysis. These include low image quality caused by noise and artifacts, limited data availability compromising robustness on unseen data, class imbalance leading to biased predictions, and insufficient feature representation, as conventional CNNs often fail to capture subtle patterns and complex dependencies. To address these challenges, we propose DAME (Diffusion-Augmented Meta-Learning Ensemble), a unified architecture that integrates hybrid modeling with generative learning using the Denoising Diffusion Probabilistic Model (DDPM). The DDPM component improves resolution, augments scarce data, and mitigates class imbalance. A hybrid backbone combining CNN, Vision Transformer (ViT), and CBAM captures both local dependencies and long-range spatial relationships, while CBAM further enhances feature representation by adaptively emphasizing informative regions. Predictions from multiple hybrids are aggregated, and a logistic regression meta classifier learns from these outputs to produce robust decisions. The framework is evaluated on the HAM10000 dataset, a benchmark for multi-class skin cancer classification. Explainable AI is incorporated through Grad CAM, providing visual insights into the decision-making process. This synergy mitigates CNN limitations and demonstrates superior generalizability, achieving 98.6% accuracy, 0.986 precision, 0.986 recall, and a 0.986 F1-score, significantly outperforming existing approaches. Overall, the proposed framework enables accurate, interpretable, and reliable medical image diagnosis through the joint optimization of contextual modeling, feature discrimination, and data generation. Full article

Unconventional reservoirs are critical for future energy supply, but present major challenges for predictions of production due to their ultra-low permeability, strong pressure sensitivity, and non-Darcy flow. Mechanistically grounded physics-based models depend on uncertain parameters derived from laboratory tests or empirical correlations, limiting their field reliability. A data-enhanced variable start-up pressure gradient framework is developed herein, integrating flow physics with physics-informed neural networks (PINNs), surrogate models, and Bayesian optimization. The framework adaptively refines key parameters to represent spatial and temporal variability in reservoir behavior. Validation with field production data shows significantly improved accuracy and robustness compared to baseline physics-based and purely data-driven approaches. Sensitivity and uncertainty analyses confirm the physical consistency of the corrected parameters and the model’s stable predictive performance under perturbations. Comparative results demonstrate that the data-enhanced model outperforms conventional models in accuracy, generalization, and interpretability. This study provides a unified and scalable approach that bridges physics and data, offering a reliable tool for prediction, real-time adaptation, and decision support in unconventional reservoir development. Full article

This study aims to evaluate the ability of an artificial intelligence (AI) model developed for use in the field of orthodontics to accurately and reliably classify skeletal maturation stages of individuals using hand–wrist radiographs. A total of 809 grayscale hand–wrist radiographs (250 × 250 px; pre-peak n = 400, peak n = 100, post-peak n = 309) were analyzed using four complementary image-based feature extraction methods: Local Binary Pattern (LBP), Histogram of Oriented Gradients (HOG), Zernike Moments (ZM), and Intensity Histogram (IH). These methods generated 2355 features per image, of which 2099 were retained after variance thresholding. The most informative 1250 features were selected using the ANOVA F-test and classified with a stacking-based machine learning (ML) architecture composed of Light Gradient Boosting Machine (LightGBM) and Logistic Regression (LR) as base learners, and Random Forest (RF) as the meta-learner. Across all evaluation folds, the average performance of the model was Accuracy = 83.42%, Precision = 84.48%, Recall = 83.42%, and F1 = 83.50%. The proposed model achieved 87.5% accuracy, 87.8% precision, 87.5% recall, and an F1-score of 87.6% in 10-fold cross-validation, with a macro-average area under the ROC curve (AUC) of 0.96. The pre-peak stage, corresponding to the period of maximum growth velocity, was identified with 92.5% accuracy. These findings indicate that integrating handcrafted radiographic features with ensemble learning can enhance diagnostic precision, reduce observer variability, and accelerate evaluation. The model provides an interpretable and clinically applicable AI-based decision-support tool for skeletal maturity assessment in orthodontic practice. Full article

This paper introduces the Lambert-Lindley distribution, a two-parameter extension of the Lindley model constructed through the Lambert-F generator. The new distribution retains the non-negative support of the Lindley distribution and provides additional flexibility by incorporating a shape parameter that controls skewness and tail behavior. Structural properties are derived, including the probability density function, cumulative distribution function, quantile function, hazard rate, and moments. Parameter estimation is addressed using the method of moments and maximum likelihood, and a Monte Carlo simulation study carried out to evaluate the performance of the proposed estimators. The practical applicability of the Lambert–Lindley distribution is demonstrated with two real datasets: stress rupture times of Kevlar/epoxy composites and hospital stay durations of breast cancer patients. Comparative analyses using goodness-of-fit tests and information criteria demonstrate that the proposed model can outperform classical alternatives such as the Gamma and Weibull distributions. Consequently, the Lambert–Lindley distribution emerges as a flexible alternative for modeling positive unimodal data in contexts such as material reliability studies and clinical duration analysis. Full article

This study analyzes how blockchain technology can be interpreted through an economic perspective, viewing network nodes as rational agents whose strategic behavior affects the efficiency and sustainability of decentralized systems. Using a multi-player non-cooperative game with complete but imperfect information, we model validators’ decisions in voting-based consensus mechanisms and compare alternative incentive configurations through simulation results. The analysis shows how variations in reward schemes influence validators’ behavior and consensus reliability. Extending the framework to Decentralized Autonomous Organizations (DAOs), the study explores how blockchain-based incentives can enhance participation, accountability, and decentralized governance. The findings highlight that incentive design plays a decisive role in aligning individual motivations with collective goals, ensuring both network integrity and long-term sustainability. Overall, this study connects economic theory with blockchain governance, extending its relevance to business and organizational contexts beyond cryptocurrencies. Full article