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Panoramic dental radiography is one of the most used images of the different dental specialties. This radiography provides information about the anatomical structures of the teeth. The correct evaluation of these radiographs is associated with a good quality of the image obtained. In this study, 598 patients were consecutively selected to undergo dental panoramic radiography at the Department of Radiology of the Faculty of Dentistry, Universidad Nacional de Asunción. Contrast enhancement techniques are used to enhance the visual quality of panoramic dental radiographs. Specifically, this article presents a new algorithm for contrast, detail and edge enhancement of panoramic dental radiographs. The proposed algorithm is called Multi-Scale Top-Hat transform powered by Geodesic Reconstruction for panoramic dental radiography enhancement (MSTHGR). This algorithm is based on multi-scale mathematical morphology techniques. The proposal extracts multiple features of brightness and darkness, through the reconstruction of the marker (obtained by the Top-Hat transformation by reconstruction) starting from the mask (obtained by the classic Top-Hat transformation). The maximum characteristics of brightness and darkness are added to the dental panoramic radiography. In this way, the contrast, details and edges of the panoramic radiographs of teeth are improved. For the tests, MSTHGR was compared with the following algorithms: Geodesic Reconstruction Multiscale Morphology Contrast Enhancement (GRMMCE), Histogram Equalization (HE), Brightness Preserving Bi-Histogram Equalization (BBHE), Dual Sub-Image Histogram Equalization (DSIHE), Minimum Mean Brightness Error Bi-Histogram Equalization (MMBEBHE), Quadri-Histogram Equalization with Limited Contrast (QHELC), Contrast-Limited Adaptive Histogram Equalization (CLAHE) and Gamma Correction (GC). Experimentally, the numerical results show that the MSTHGR obtained the best results with respect to the Contrast Improvement Ratio (CIR), Entropy (E) and Spatial Frequency (SF) metrics. This indicates that the algorithm performs better local enhancements on panoramic radiographs, improving their details and edges.

Working conditions of rolling bearings of wind turbine generators are complicated, and their vibration signals often show non-linear and non-stationary characteristics. In order to improve the efficiency of feature extraction of wind turbine rolling bearings and to strengthen the feature information, a new structural element and an adaptive algorithm based on the peak energy are proposed, which are combined with spectral correlation analysis to form a fault diagnosis algorithm for wind turbine rolling bearings. The proposed method firstly addresses the problem of impulsive signal omissions that are prone to occur in the process of fault feature extraction of traditional structural elements and proposes a “W” structural element to capture more characteristic information. Then, the proposed method selects the scale of multi-scale mathematical morphology, aiming at the problem of multi-scale mathematical morphology scale selection and structural element expansion law. An adaptive algorithm based on peak energy is proposed to carry out morphological scale selection and structural element expansion by improving the computing efficiency and enhancing the feature extraction effect. Finally, the proposed method performs spectral correlation analysis in the frequency domain for an unknown signal of the extracted feature and identifies the fault based on the correlation coefficient. The method is verified by numerical examples using experimental rig bearing data and actual wind field acquisition data and compared with traditional triangular and flat structural elements. The experimental results show that the new structural elements can more effectively extract the pulses in the signal and reduce noise interference, and the fault-diagnosis algorithm can accurately identify the fault category and improve the reliability of the results.

The automated detection of early-stage foliar diseases in open fields is severely constrained by two theoretical bottlenecks: the physical erasure of high-frequency spatial details during downsampling and the topological mismatch between rigid bounding boxes and irregular lesion morphologies. To systematically overcome these structural limitations, we propose FD-DEIM, a lightweight detection framework optimized for fine-grained feature preservation and dynamic boundary adaptation. The architecture introduces four functionally distinct mechanisms: (1) FD-SRFD, an attention-enhanced stem that anchors sub-pixel coordinates via slicing operations in the initial feature extraction stage, preventing the annihilation of microscopic high-frequency details; (2) DRFD, a deep robust downsampler deployed in the network neck, utilizing a parallel multi-path design to protect high-fidelity spatial features from semantic drift during dimension reduction; (3) FD-Block, an ultra-lightweight fusion module that leverages Partial Convolutions to eliminate computational redundancy while efficiently aggregating multi-scale semantics; and (4) D-FINE, a dynamic offset decoder that forces sampling points to actively deform and tightly hug non-convex, organic fungal contours, fundamentally resolving geometric inductive bias. To ensure ecological validity, we also introduce the RTFD dataset, utilizing generative style transfer to simulate extreme meteorological stressors. Extensive evaluations demonstrate that FD-DEIM achieves a superior AP@50 of 0.667. Crucially, by maintaining an unbroken spatial coordinate preservation chain, the model achieves an AP S of 0.391 for microscopic targets, a 31.6% relative improvement over the state-of-the-art YOLOv13n. Operating at a minimal computational cost of 7.67 GFLOPs with a 307 ms inference latency on an RK3576 edge CPU, FD-DEIM establishes a new Pareto frontier between rigorous edge-computing constraints and high-fidelity microscopic detection, facilitating proactive precision intervention in agriculture.

The scarcity of defect samples and the imbalance of defect types lead to the fact that achieving defect detection in color-patterned fabrics remains a challenge in the textile industry. Defect detection methods based on traditional auto-encoder are difficult to solve the problem of defect detection in complex color-patterned fabrics. In order to solve the problem of weak feature representation of traditional auto-encoders, this paper proposes an unsupervised method based on triplet attention multi-scale U-shape denoising convolutional auto-encoder (TA_MSUDCAE). The method further enhances the feature representation capability of the auto-encoder by introducing a triplet attention mechanism based on utilizing the multi-scale information of the image. Firstly, the defect-free samples that are added with Gaussian noise are used as inputs to the model in the training stage. The model is trained to reconstruct and repair the defective regions. Secondly, the test image is input to the trained model to obtain a normal reconstructed image, and the residual image is obtained by calculating the difference between the input image and the reconstructed image. Finally, the defect detection and localization results are obtained by threshold segmentation and mathematical morphology processing of the residual image. A large number of experiments have been carried out on a variety of representative color-patterned fabrics, and the results prove the effectiveness of the proposed method in fabric defect detection.

In response to the challenge of low accuracy in retinal vessel segmentation attributed to the minute nature of the vessels, this paper proposes a retinal vessel segmentation model based on an improved U-Net, which combines multi-scale feature extraction and fusion techniques. An improved dilated residual module was first used to replace the original convolutional layer of U-Net, and this module, coupled with a dual attention mechanism and diverse expansion rates, facilitates the extraction of multi-scale vascular features. Moreover, an adaptive feature fusion module was added at the skip connections of the model to improve vessel connectivity. To further optimize network training, a hybrid loss function is employed to mitigate the class imbalance between vessels and the background. Experimental results on the DRIVE dataset and CHASE_DB1 dataset show that the proposed model has an accuracy of 96.27% and 96.96%, sensitivity of 81.32% and 82.59%, and AUC of 98.34% and 98.70%, respectively, demonstrating superior segmentation performance. Graphical Abstract

Primary dendrite arm spacing (PDAS) is a crucial microstructural feature in nickel-based superalloys produced by laser cladding. In order to investigate the effects of process parameters on PDAS, a multi-scale model that integrates a 3D transient heat and mass transfer model with a quantitative phase-field model was proposed to simulate the dendritic growth behavior in the molten pool for laser cladding Inconel 718. The values of temperature gradient (G) and solidification rate (R) at the S/L interface of the molten pool under different process conditions were obtained by multi-scale simulation and used as input for the quantitative phase field model. The influence of process parameters on microstructure morphology in the deposition layer was analyzed. The result shows that the dendrite morphology is in good agreement with the experimental result under varying laser power (P) and scanning velocity (V). PDAS was found to be more sensitive to changes in laser scanning velocity, and as the scanning velocity decreased from 12 mm/s to 4 mm/s, the PDAS increased by 197% when the laser power was 1500 W. Furthermore, smaller PDAS can be achieved by combining higher scanning velocity with lower laser power.

Industry benefits cannot be obtained from shale gas reservoir without stimulations, due to the ultra-low porosity and permeability of shale. A series of integrated technical measures have been developed for developing shale gas economically, such as horizontal wells and multistage hydraulic fracturing. Combining the above works, we can achieve higher productivity by enlarging stimulated reservoir volume (SRV) and linking fracture network in shale gas reservoirs. In this paper, a novel analytical mathematical model for production forecast of multistage horizontal well was developed based on the seepage theory of fractured well in the dual-medium gas reservoirs. In this model, multi-scale migration mechanism and the complicated morphology of hydraulic fractures in fractured shale gas reservoir were considered. It has been closely solved by the method of well test analysis and mathematical physics. To validate the accuracy of the model in this paper, a well from Changning–Weiyuan shale gas reservoir in China is taken as a real-case application. The calculation results of the model and the actual production data of the well are in good accordance. Meanwhile, the impacts of sensitive factors including desorption, Knudsen diffusion, slip flow, stress sensitivity of micro-fractures and high-velocity non-Darcy flow within hydraulic fractures on cumulative production were analyzed. At last, fracture morphology has been optimized through the model.

Seismic data reconstruction can provide high-density sampling and regular input data for inversion and imaging, playing a crucial role in seismic data processing. In seismic data reconstruction, a common scenario involves a significant distance between the source and the first receiver, which makes it unattainable to acquire near-offset data. A new workflow for seismic data extrapolation is proposed to address this issue, which is based on a multi-scale dynamic time warping (MS-DTW) algorithm. MS-DTW can accurately calculate the time-shift between two time series and is a robust method for predicting time-offset (t−x) domain data. Using the time-shift calculated by the MS-DTW as the basic input, predict the two-way traveltime (TWT) of other traces based on the TWT of the reference trace. Perform autoregressive polynomial fitting on TWT and extrapolate TWT based on the fitted polynomial coefficients. Extract amplitude information from the TWT curve, fit the amplitude curve, and extrapolate the amplitude using polynomial coefficients. The proposed workflow does not necessitate data conversion to other domains and does not require prior knowledge of underground geological information. It applies to both isotropic and anisotropic media. The effectiveness of the workflow was verified through synthetic data and field data. The results show that compared with the method of predictive painting based on local slope, this approach can accurately predict missing near-offset seismic signals and demonstrates good robustness to noise.

Analysis of medical CT images directly affects the accuracy of clinical case diagnosis. Therefore, feature extraction problem of medical CT images is extremely important. A feature extraction algorithm for medical CT images of sports tear injury is proposed. First, CT images are decomposed into a low frequency component and a series of high frequency components in different directions by wavelet fast decomposition method. The high- and low-frequency information of CT images is enhanced by wavelet layered multi-directional image enhancement algorithm, and the multi-scale enhancement for medical CT images of sports tear injury is completed. Then, edge of the enhanced CT images is extracted using an image edge extraction algorithm based on extended mathematical morphology. Finally, based on the extracted edge information of CT images, feature extraction for medical CT images of sports tear injury is completed by the NSCT-GLCM based CT image feature extraction algorithm. Research results show that the proposed algorithm effectively extracts CT image features of sports tear injury and provides auxiliary information for doctor diagnosis.

This study delves into the grinding-induced microstructural and mechanical evolution in high-entropy nickel-based superalloys GH4169 and DD5, underscoring their distinct behaviors under varying machining conditions. Leveraging “Random Substitution” in Material Studio, the research developed intricate atomic models to accurately depict the complex chemical compositions and microstructures of these superalloys. Neper software was employed for multi-scale modeling, specifically analyzing the unit cells of GH4169. A critical focus was placed on the effects of key grinding parameters—depth, spindle speed, and feed rate—on the crystallographic deformation of GH4169, contrasting it with the response of DD5. The study highlighted a notable transition in GH4169’s material removal mechanism from plastic flow to chip spallation at enhanced grinding depths and feed rates, while maintaining lattice integrity at higher grinding speeds. GH4169 consistently demonstrated greater tangential and normal forces during grinding compared to DD5, reflecting intricate machining complexities. The differential crystal orientations between these superalloys significantly impacted the grinding force distribution and heat dissipation during the process. This comprehensive analysis provides pivotal insights into the micro-level grinding process parameters, enriching both theoretical and practical understanding of material machinability in advanced manufacturing contexts. The study’s novelty lies in its application of detailed atomic models and multi-scale modeling to uncover subtle microstructural and mechanical dynamics during the grinding of superalloys.