Search Results (5,089)

The carbonate massifs of the southern Italian Apennines host extensive karst aquifers, which represent the principal drinking water resources. This study focuses on the Dragone Plain polje, a vast closed karst depression located in the main recharge sector of the Terminio–Tuoro carbonate massif. The polje drains a ~55 km² endorheic catchment and may be flooded during the cold and wet season, forming a temporary lake. We employed continuous hydroclimatic time series (rainfall, groundwater level, spring discharge, and river level) together with sparse Sentinel-2 true color satellite images for the period 2020–2024 to analyze the flooding process in the polje and its hydraulic connection with the saturated zone of the karst aquifer. Results indicate that lake formation depends on the balance among soil moisture, rainfall intensity, and runoff development, which were modeled on a daily scale. Daily recharge was also estimated and compared with groundwater level time series from the deep karst aquifer. The modeling was integrated with cross-correlation analysis of the time series, providing insights into the propagation of precipitation pulses through the hydrogeological system. This case study represents an important example for understanding the relationship between karst polje hydrological functioning and climate in a Mediterranean area. Full article

Tunnels, soundproof screens and other vertical roadside traffic facilities play an important role in isolating the driving environment, maintaining driving safety, and reducing driving noise. As the usage time increases, these facade traffic buildings become polluted and cause traffic safety problems. Obstacles on three-dimensional walls of different shapes, colors, and sizes are the most challenging problem in intelligent cleaning environment perception. This paper proposes an obstacle segmentation method based on a visual language model to overcome these problems. Firstly, in the constructed experimental environment, a visual–language obstacle dataset is collected, named the Road-side General Obstacles Dataset (RGOD), and the collected dataset is labeled with both a segmentation mask and a language description. These preprocessing results are used as the training input of the perception model to obtain the foreground and background separation results. Secondly, a VLM-GOS model was proposed to segmentation special-shaped obstacles, which emphasizes the distinction between background and foreground targets. Finally, the general obstacle is segmented by a vision–language model with a similar loss function, and evaluated with different metrics. Experimental results show that compared with models such as MaskFormer, SegFormer, and ASD-Net, this method improves the model’s perceptual ability and increases accuracy by 3%. More importantly, the model is more interpretable. Full article

This work examined the impact of Mayang Citrus peel powder (MCPP) concentrations on the physicochemical, textural, antioxidant, and flavor volatile properties of stirred yogurt while involving the application of machine learning approaches for its targeted prediction and comprehensive study. The addition of MCPP led to a dose-dependent decrease in pH, lightness, red–green color values, and water holding capacity, while increasing titratable acidity, syneresis, yellow–blue color values, viable LAB cells, polyphenol bioaccessibility, and in vitro antioxidant activity. The ratio of MCPP at 0.1% significantly increased viscosity, indicating yogurt with modified flow properties. Texture analysis revealed that yogurts fortified with 0.1% and 0.5% MCPP showed similar characteristics to the control, while a 1% concentration enhanced yogurt stability. Especially, MCPP supplementation enhanced the concentration of flavor volatiles in yogurt, and the 1% MCPP-enriched sample exhibited the highest overall quality in sensory evaluation among all formulations. A total of six machine learning predictive models were employed to comprehensively reveal the effects of MCPP addition on yogurt physicochemical and antioxidant properties, and the Lasso model achieved the highest composite score with high accuracy (R² = 0.9265, RMSE = 0.0011, MSE = 1.395 × 10⁻⁶). Full article

Stain variability in digital pathology affects both cross-center diagnostic consistency and the robustness of downstream computational analysis. In this work, we formulate stain normalization as a variational inverse problem and derive a Screened Poisson Normalization (SPN) model from the steady-state reaction–diffusion mechanism underlying histological staining. In the CIE $L^{*}{a}^{*}{b}^{*}$ space, the model couples a gradient-domain fidelity term with a chromatic anchoring term, yielding a screened Poisson equation that preserves tissue morphology while enforcing color consistency. We prove that the corresponding variational problem is well-posed in $H^1\left(\Omega \right)$ and stable with respect to perturbations of the input data. We further show that the screening term induces an intrinsic localization length ${\ell }_c\sim {\lambda }_c^{-1/2}$, so that boundary perturbations decay exponentially away from tile interfaces. Based on this locality, we develop a non-overlapping tiled DCT-based spectral solver for gigapixel whole-slide images, enabling consistent tile-wise stain normalization and seamless whole-slide reassembly without heuristic boundary blending. Experiments on multi-scanner, multi-protocol, and archival-fading pathology datasets show that SPN achieves stable stain normalization with competitive chromatic alignment and strong preservation of diagnostically relevant microstructure, particularly in full-slide and tiled reconstruction settings. Supplementary experiments on synthetic pathology-like images further support the robustness of SPN under controlled color perturbations and indicate good generalization across diverse staining variations. Full article

The CALIPSO V4.10 5 km cloud-layer product contains a small yet influential fraction of low-confidence and “unknown” cloud-type labels, which constrains its effectiveness in climatological analyses and limits its utility for downstream Earth system applications. To improve the practical usability and completeness of these observations, this study develops a multilayer perceptron (MLP)-based refinement framework using global summer daytime CALIPSO data from 2006–2021. High-confidence cloud samples (76% of the dataset), defined as cases with high Feature Type QA and high Ice/Water Phase QA, were used as the reliable supervision subset to train the MLP model using 11 geolocation-, optical-, and microphysics-related variables, including cloud optical depth, cloud thickness, depolarization ratio, and color ratio. The trained model was subsequently applied to a separately defined low-confidence cloud subset (~5% of the dataset), consisting of cases with high Feature Type QA but low Ice/Water Phase QA, of which over 60% were originally labeled as “unknown”, to generate probabilistic assignments of three cloud types: ice clouds, water clouds, and oriented ice crystals. Evaluation using withheld high-confidence samples indicates a strong level of agreement with operational CALIPSO classifications (~94.99%). Moreover, the refined low-confidence results exhibit physically coherent vertical structural characteristics consistent with established cloud thermodynamic regimes. It is emphasized that the proposed framework does not establish an independent physical truth beyond CALIOP’s measurement capability; instead, it provides a physically consistent and statistically robust approach to improving the completeness and practical usability of CALIPSO cloud-type products for large-scale scientific and modeling applications. Full article

As modern urban systems advance, video surveillance has become indispensable for ensuring high-quality urban development. Nighttime images acquired in urban monitoring scenarios are often degraded by haze and non-uniform illumination, resulting in reduced visibility, color distortion, and blurred structural boundaries. To address these issues, this paper proposes a nighttime image dehazing framework that combines mixed-norm variational atmospheric-light estimation with adaptive boundary-constrained transmission refinement. Specifically, an  L₂ − L_p mixed-norm regularization model is introduced to improve atmospheric-light estimation under complex nighttime illumination and suppress halo diffusion and color distortion around strong light sources. In addition, an adaptive boundary-constrained transmission refinement strategy with weighted soft-threshold shrinkage is developed to reduce residual artifacts while preserving structural edges. Experimental results on synthetic and real nighttime haze datasets demonstrate that the proposed method consistently outperforms representative state-of-the-art methods in both visual quality and quantitative metrics, showing superior robustness and restoration performance for nighttime urban monitoring applications. Full article

Dynamic scene reconstruction has achieved significant milestones with the advent of 3D Gaussian Splatting (3DGS). However, extending this technology from geometric reconstruction to semantic understanding in dynamic environments remains a challenge. Existing methods often rely on external 2D trackers, which lead to temporal inconsistencies and semantic drift, or suffer from the high computational costs of high-dimensional feature fields. In this paper, we propose a novel framework, Gaussian Semantic Segmentation based on Color and Shape Deformation Fields (GSSBC), to address these issues. Building upon our GBC dynamic scene representation, we bind learnable semantic features to deformable Gaussian primitives. We introduce a spatiotemporal contrastive learning strategy guided by the Segment Anything Model (SAM) to enforce semantic consistency without explicit tracking. Furthermore, we employ a density-based clustering algorithm with label propagation to extract discrete object entities efficiently. Experimental results on the HyperNeRF and Neu3D datasets demonstrate that our method achieves superior segmentation accuracy and spatiotemporal stability compared to state-of-the-art approaches, enabling effective semantic understanding in complex dynamic scenes. Full article

Clothing classification by occasion is an important area in computer vision and artificial intelligence (AI). This task is particularly challenging because of the subtle visual similarities among clothing categories such as formal, party, and casual attire. Variations in color, fabric, patterns, and lighting further increase the complexity of this task. To address this challenge, we used the Fashionpedia dataset to create a balanced subset of 15,000 images. Specifically, we adopted two different methods for labeling these images: automated classification, which relies on category identifications (IDs) and components, and manual labeling performed by human annotators. We then implemented our preprocessing pipeline, which includes several steps: resizing, image normalization, background removal using segmentation masks, and class balancing. We benchmarked traditional models, including artificial neural networks (ANNs), support vector machines (SVMs), and k-nearest neighbors (KNNs), which use a histogram of oriented gradient (HOG) features, as well as deep learning models such as convolutional neural networks (CNNs), the Visual Geometry Group 16 (VGG16) model utilizing transfer learning, and the vision transformer (ViT) model, all evaluated using identical data splits and preprocessing procedures. The traditional models achieved moderate accuracy, ranging from 54% to 66%. In contrast, the ViT model achieved an accuracy of 81.78% with automated classification and 98.09% with manual labeling. This indicates that a higher label accuracy, along with the preprocessing steps used, significantly enhances the performance. Together, these factors improve the effectiveness of ViT in context-aware apparel classification and establish a reliable baseline for future research. Full article

Image colorization transforms monochrome images into full-colored versions, which improves image restoration in fields such as art, history, and medicine. AI models, such as convolutional neural networks and generative adversarial networks, are widely used, but they have limitations in generalization and interpretability. Therefore, we applied the Convolutional Kolmogorov–Arnold Network (CKAN), a new neural architecture that adds a convolutional layer to the Kolmogorov–Arnold Network for colorizing grayscale images of fruits and vegetables. A dataset of different varieties of fruits and vegetables was used, and the model’s performance was evaluated using the structural similarity index (SSIM) and mean squared error (MSE). After testing the model, the results showed that the CKAN colorized images achieved the desired outcome, consistently having a high SSIM score (up to 0.9) and a low MSE score (<100.0). This confirms CKAN’s potential for effective image colorization and highlights its possible applications in other computer vision tasks. Full article

Foreign plastic objects (FPOs) in poultry products present significant food safety risks and cause economic losses for the industry. Conventional detection methods, including X-rays and color imaging, often struggle to identify small or low-density plastics. Hyperspectral imaging (HSI) offers both spatial and spectral information but suffers from high computational cost when applied for FPO identification in industrial environments. This study introduces a parameter-efficient and computationally efficient spatial–spectral transformer framework for pixel-level classification of FPOs on broiler meat using NIR-HSI (1000–1700 nm). The framework integrates three innovations: (1) center-focused linear attention (CFLA) to reduce computational complexity from $O\left(n^2\right)$ to $O\left(n\right)$; (2) patch-local mixed-axis 2D rotary position embedding to preserve geometric relationships within hyperspectral patches; and (3) low-rank factorized projection (LRP) matrices to reduce parameters by approximately 50% within projection weight matrices. The framework was trained and evaluated on a dataset of 52 chicken fillets, comprising 295,340 labeled target hyperspectral pixels from 12 common polymer types and 1 fillet class. The model achieved 99.39% overall accuracy, 99.57% average accuracy, and a 99.31 Kappa coefficient across 248,540 test pixels. Per-class precision, recall, and F1-score exceeded 98.05%, 98.59%, and 98.76%, respectively, across all classes. Efficiency analyses showed an 83% reduction in multiply–accumulate operations (MACs), a 22% reduction in trainable parameters, and a model size reduction from 1.72 MB to 1.35 MB relative to the baseline configuration. These gains also translated into practical inference benefits, with the final model achieving a throughput of 212,971.5 hyperspectral patch cubes/s and a 4.19× speedup over the baseline. These results demonstrate that the proposed framework combines strong classification performance with high efficiency, supporting high-throughput inference for real-time monitoring and enabling contamination source traceability and preventive quality control in industrial poultry processing. The approach provides a benchmark for applying transformer-based models to food safety inspection tasks. Full article

The instability of pigments and non-quantitative indication limit the application of intelligent labels in food freshness monitoring. Natural anthocyanins face challenges including photodegradation and difficulty in quantifying shrimp freshness. To solve these problems, this study prepared a colorimetric intelligent label with UV-shielding and real-time monitoring functions. Carbon-coated nano-TiO₂ (C-TiO₂) was synthesized by the hydrothermal method and combined with blueberry anthocyanins (BAs) in an agarose (AG)/gellan gum (GG)/glycerol matrix. The label properties were characterized and a remaining shelf-life prediction model was established based on the correlation between label color difference (ΔE) and shrimp total volatile basic nitrogen (TVB-N). The results demonstrated that C-TiO₂ could enhance compatibility and color stability, and maintain mechanical properties. After 24 h of ultraviolet irradiation, the BA degradation rate was 98.4% in the GAB group and 62.8% in the GABT-0.05 group, representing a reduction of 35.6% compared to the former. This indicates that the addition of C-TiO₂ significantly enhanced photostability. The predictive model demonstrated an error below 10% at both 10 °C and 20 °C conditions, indicating its potential for shelf-life prediction applications. This dual-functional label provides a reliable method for visual and quantitative evaluation of shrimp freshness. Full article

We investigate the effects of dark matter on the properties of strange quark stars within the framework of general relativity with two fluids coupled only by gravity. Adopting the color–flavor-locked model for strange quark matter and considering both fermionic (free fermion gas) and bosonic (polytropic) equations of state for dark matter, we systematically study the structure and tidal deformability of dark matter-admixed strange stars. Our results show that the presence of dark matter significantly modifies the mass–radius relations, with the maximum mass of dark matter-admixed strange stars exhibiting a non-monotonic dependence on the dark matter mass fraction $\chi$, which reaches a minimum at an intermediate value of $\chi$. The tidal deformability $\Lambda$ of dark matter-admixed strange stars shows complex behavior depending on both the stellar mass and dark matter fraction, with $\Lambda -\beta$ (the compactness parameter) relations deviating from the universal relations observed for pure strange stars or dark stars. Our findings demonstrate that dark matter-admixed strange stars with different configurations but identical masses and radii can be distinguished by their tidal deformabilities, providing potential observational signatures for detecting dark matter in compact astrophysical objects. The results are compared with current astrophysical constraints from gravitational wave observations and pulsar measurements. Full article

Cherry shrimp (Neocaridina davidi) are a popular ornamental freshwater species known for their bright colors and ability to thrive in a variety of tank environments. However, due to their small size and the subtle differences between males and females, it can be challenging to determine their sex. A You Only Look Once Version 9 (YOLOv9) object identification model and a Raspberry Pi 4-based system are used in this study to infer and classify the sex of cherry shrimp. A graphical user interface facilitates image collection and classification and displays the results. We developed a Raspberry Pi 4-based device with a camera module that captures images of cherry shrimp and integrated a DynamicDet architecture with Programmable Gradient Information and Generalized Efficient Layer Aggregation Networks to classify the sex of cherry shrimp. We evaluated the performance of the model using a confusion matrix to measure the accuracy of the sex classification. A confusion matrix was used to assess the collected data, and the system achieved an accuracy of 85.00%. The researchers suggest expanding the dataset to include more color variations, focusing on adding more robust male shrimp datasets, enabling the device to function without an enclosure, and updating the technology for faster inference. Full article

Rolling bearing fault diagnosis under complex and noisy operating conditions requires not only high diagnostic accuracy but also interpretability that can be quantitatively verified against physically meaningful excitation structures. However, many existing deep learning approaches rely on a single time–frequency (TF) representation and provide limited, non-verifiable links between model decisions and the original vibration patterns. To address this issue, we propose MBT-XAI, a multi-wavelet TF fusion network with a Token-to-Spectrum Traceback (TST) mechanism for structure-preserving, physics-consistent interpretability. Three complementary wavelets, namely Morlet, Mexican Hat, and Complex Morlet, are used to construct multi-view TF representations, which are encoded into RGB channels and adaptively fused via cross-channel attention within a Transformer backbone. TST maps patch-token attributions back to the TF domain, enabling quantitative evaluation of physics consistency through overlap-based metrics. Experiments on the public CWRU dataset and an industrial IMUST dataset show that MBT-XAI achieves 98.13 ± 0.24% and 96.23 ± 0.31% accuracy at SNR = 0 dB, outperforming the strongest baseline by 2.83% and 2.43%, respectively. Under AWGN contamination, MBT-XAI maintains 95.44 ± 0.38%/93.45 ± 0.47% accuracy on CWRU and 95.80 ± 0.33%/92.91 ± 0.51% accuracy on IMUST at SNR = −2/−4 dB. Under colored-noise contamination, the proposed method also preserves robust performance under pink and brown noise at the same SNR levels. Quantitative interpretability evaluation further indicates high alignment between salient frequency regions and theoretical fault-characteristic bands, with IoU = 80.21 ± 0.86% and Coverage = 91.70 ± 0.63%. In addition, MBT-XAI requires 10.393 M parameters and 10.678 GFLOPs, with an inference latency of 14.7 ms per sample (batch size = 1) on an NVIDIA GeForce RTX 3060 GPU. These results suggest that multi-wavelet TF modeling with attention-based fusion and TF-level traceback provides an accurate, robust, and physics-consistent framework for intelligent bearing fault diagnosis. Full article

The development of effective fluorescent probes for the detection of acids and bases, both in solution and in the solid state, is of particular interest worldwide, due to the possibility of preventing hazardous consequences for human health and the environment. In the present work, the synthesis of a 1,8-naphthalimide derivative, designed as a “fluorophore-receptor₁-spacer-receptor₂” model, is considered. The compound contains two receptors for analytes in one molecule and can operate as a fluorescent probe via PET and ICT mechanisms. The photophysical behavior of the synthesized derivative in solution, on strip paper, and in thin film was investigated. It was found that the transition from acidic to alkaline medium in solution is associated with a change in color that is visible with the naked eye (yellow–orange-red–blue). The change in fluorescence, both in solution and spread on a supporting surface (strip paper and thin film), can be spectrophotometrically observed. The influence of various volatile acids on the sensing activity of the synthesized compound in solution and deposited on a solid support was investigated. It was found that with increasing acid strength, the fluorescence intensity increases. The strip paper and thin film obtained with the synthesized compound show reversible switching between the “off” and “on” states of fluorescence. The strip paper exhibited good cycling under acid–base vapor stimulation. The results obtained demonstrate the possibility of application of the synthesized compound as a colorimetric and fluorescent probe for determination of pH in solution, and detection of acids, bases, and their vapors in indoor and outdoor residential and industrial premises, as well as in the environment. Full article