Search Results (4,154)

Surface defects on stay cables are primary contributors to wire corrosion and breakage. Traditional manual inspection methods are inefficient, inaccurate, and pose safety risks. Recently, cable-climbing robots have shown significant potential for surface defect detection, but existing designs are constrained by large size, limited operational speed, and complex installation, restricting their field applicability. This study presents an intelligent robotic system for detecting cable surface defects. The system features a dual-wheel driving mechanism, and a computer vision–based defect recognition framework is proposed. Image preprocessing techniques, including histogram equalization, Gaussian filtering, and Sobel edge detection, are applied. Interfering information, such as sheath edges and rain lines, is removed using the Hough Line Detection Algorithm and template matching. The geometry of identified defects is automatically calculated using connected component analysis and contour extraction. The system’s performance is validated through laboratory and field tests. The results demonstrate easy installation, adaptability to cable diameters from 70 mm to 270 mm and inclination angles from 0° to 90°, and a maximum speed of 26 m/min. The proposed defect recognition framework accurately identifies typical defects and captures their morphological characteristics, achieving an average precision of 92.37%. Full article

Wear inevitably occurs in ball screw assemblies after long-term operation, leading to a decline in transmission performance and machining accuracy. Therefore, the accurate identification of wear states is crucial. In this study, we propose a wear state identification method based on the surface profile of the ball screw. This method effectively overcomes the limitations of traditional experimental approaches that require frequent disassembly of the ball screw or rely on vibration and current signals, which are prone to external interference. Surface profile data covering the entire service life of the screw were obtained through performance degradation experiments. A hybrid feature set was constructed by extracting parameters such as roughness, peak-to-valley height, root mean square, recurrence rate, and fractal characteristics, and classification was performed using a genetic-algorithm-optimized support vector machine (GA-SVM). The experimental results demonstrate that the proposed method can accurately characterize wear evolution, achieving an average identification accuracy of 98.48% while maintaining robustness and effectively avoiding interference from extraneous signals. Full article

Discontinuity trace provides critical geological data for engineering design and construction optimization. However, current extraction methods relying on discontinuity intersection fitting are highly sensitive to the segmentation accuracy of individual discontinuity, while trace segment connectivity remains suboptimal. To address these challenges, we propose an ARCG (Adaptive Region Contour Growing) method using 3D point clouds. By dynamically adjusting parameter thresholds, our approach simultaneously extracts both discontinuities and their boundaries. We then evaluate the fitting performance of different discontinuity models using area ratios, identifying the parallelogram as the most suitable representation. The method then detects intersection lines between paired discontinuities through spatial intersection analysis, with dynamic partitioning preserving original geometric properties. Finally, a bidirectional weighted graph-based growth algorithm connects intersection lines belonging to the same discontinuity, generating the final trace results. The proposed method was validated using slope data from two case studies. Results demonstrate that, compared to existing methods and point cloud processing software, our approach achieves robust extraction of complex traces while maintaining high connectivity. Moreover, it improves computational efficiency by 48.8% without compromising trace accuracy. Thus, this method offers a novel solution for the digital characterization of rock mass discontinuity parameters. Full article

The paper presents the ecclesiological convictions of the so-called New Prophecy or ‘Montanist’ movement, a prophetic movement that rapidly gained prominence throughout the Empire from the middle of the second century CE. By regarding themselves as the mouthpieces of the Johannine Paraclete-Spirit, the founding prophets conveyed primarily ethical messages to the contemporary church. It is argued that their ascetic imperatives can be regarded as the practical implementation of a more complex ecclesiological and eschatological conviction. In the Montanists’ understanding, their prophetic communities were the earthly realisation of the heavenly Jerusalem, to which the Apocalypse of John attached concrete ethical contours (Rev 21:7–8). Such ‘realistic’ eschatology prevented the prophets and their adherents from seeing the reality of the church in a dualistic way or upholding the sanctification of the individual believer as a futuristic fulfilment. They believed that the coming of the Paraclete had instituted a new era for the church, and the ‘spiritual’ believers must prove to be ready to achieve moral perfection. Such pneumatic-prophetic and ascetic-perfectionist convictions give the contours of Montanist ecclesiology, one that proved to be widespread in the second century and influential on later Christian ecclesiological trajectories as well. Full article

The accurate registration of image pairs is an indispensable key step in the process of disaster assessment, environmental monitoring, and change detection. However, obtaining correct matches from input images is difficult, especially from images with significant resolution and regional variations. The current image-registration algorithms perform poorly in this application scenario. In this article, a spatial point feature-based registration method is proposed for remote sensing images with large regional variations. First, a new edge keypoint extraction method is designed that selects points with gradient magnitude maxima around the neighborhood of the edge line segments as keypoint features. Then, the feature descriptors for each keypoint are constructed based on the geometrical distribution (distance and orientation) of each keypoint. Considering the stability of the distribution of the edge contours, our constructed descriptor vectors can be well used for image pairs with large resolution and regional variations. In addition, all feature descriptors in this method are constructed and matched in the rotated image pyramid. Finally, the fast sampling consensus algorithm is applied to eliminate mismatches. In test images with various scales, rotation angles, and regional variations, the proposed method achieved pixel-level root mean square error, and the average registration precision is nearly 100%. Meanwhile, our proposed method’s rotation and scale invariance are verified by rotating and downsampling the image pairs extensively. In addition, compared with the comparison algorithms, the method proposed in this paper has better registration performance for images with resolution and regional variations. Full article

The flow of a two-dimensional incompressible hybrid nanofluid over a stretching cylinder containing microorganisms with parallel effect of inclined magnetohydrodynamic was examined in the current study in relation to chemical reactions, heat source effect, nonlinear heat radiation, and multiple convective boundaries. The main objective of this research is the optimization of heat transfer with inclined MHD and variation in different physical parameters. The governing partial differential equations are transformed into a set of ordinary differential equations by applying the appropriate similarity transformations. The Runge–Kutta method is recognized for using shooting as a technique. Surface plots, graphs, and tables have been used to illustrate how various parameters affect the local Nusselt number, mass transfer, and heat transmission. It is demonstrated that when the chemical reaction parameter rises, the concentration and motile concentration profiles drop. The least responsive is the rate of heat transfer to changes in the inclined magnetic field and most associated with changes in the Biot number and radiation parameter shown in contour plot. The streamline graph illustrates the way fluid flow is affected simultaneously by the magnetic parameter M and an angled magnetic field. Local Nusselt number and local skin friction are improved by the curvature parameter and mixed convection parameter. The contours highlight the intricate interactions between restricted magnetic field, significant radiation, and substantial convective condition factors by displaying the best heat transfer. The three-dimensional surface, scattered graph, pie chart, and residual plotting demonstrate the statistical analysis of the heat transfer. The results support their use in sophisticated energy, healthcare, and industrial systems and enhance our theoretical knowledge of hybrid nanofluid dynamics. Full article

The safety and reliability of modern power systems are increasingly challenged by adverse environmental conditions. (1) Background: Ice accumulation on power transmission lines is recognized as a severe threat to grid stability, as tower collapse, conductor breakage, and large-scale outages may be caused, thereby making accurate monitoring essential. (2) Methods: A rule-driven and interpretable stereo vision framework is proposed for three-dimensional (3D) detection and quantitative measurement of transmission line icing. The framework consists of three stages. First, adaptive preprocessing and segmentation are applied using multiscale Retinex with nonlinear color restoration, graph-based segmentation with structural constraints, and hybrid edge detection. Second, stereo feature extraction and matching are performed through entropy-based adaptive cropping, self-adaptive keypoint thresholding with circular descriptor analysis, and multi-level geometric validation. Third, 3D reconstruction is realized by fusing segmentation and stereo correspondences through triangulation with shape-constrained refinement, reaching millimeter-level accuracy. (3) Result: An accuracy of 98.35%, sensitivity of 91.63%, specificity of 99.42%, and precision of 96.03% were achieved in contour extraction, while a precision of 90%, recall of 82%, and an F1-score of 0.8594 with real-time efficiency (0.014–0.037 s) were obtained in stereo matching. Millimeter-level accuracy (Mean Absolute Error: 1.26 mm, Root Mean Square Error: 1.53 mm, Coefficient of Determination = 0.99) was further achieved in 3D reconstruction. (4) Conclusions: Superior accuracy, efficiency, and interpretability are demonstrated compared with two existing rule-based stereo vision methods (Method A: ROI Tracking and Geometric Validation Method and Method B: Rule-Based Segmentation with Adaptive Thresholding) that perform line icing identification and 3D reconstruction, highlighting the framework’s advantages under limited data conditions. The interpretability of the framework is ensured through rule-based operations and stepwise visual outputs, allowing each processing result, from segmentation to three-dimensional reconstruction, to be directly understood and verified by operators and engineers. This transparency facilitates practical deployment and informed decision making in real world grid monitoring systems. Full article

Background: This study aimed to explore the application value of magnetic resonance imaging (MRI)-based three-dimensional segmentation and reconstruction technology for spatial structural identification and volumetric quantification of glandular and adipose tissues in bilateral gynecomastia (GM) patients undergoing surgical treatment, hoping to provide precise imaging data to support clinical surgical decision-making. Methods: A retrospective analysis was performed on preoperative MRI images and general clinical data of 52 patients with bilateral gynecomastia at the patient level (bilateral totals, N = 52) who underwent surgical treatment in the Department of Aesthetic and Reconstructive Breast Surgery, Plastic Surgery Hospital of Chinese Academy of Medical Sciences, from March 2023 to September 2024. All images were acquired using a SIEMENS Aera 1.5 T MRI scanner with T1-weighted three-dimensional fat-suppressed sequence (t1_fl3d_tra_spair). Semi-automatic segmentation and active contour modeling (Snake model) using ITK-SNAP 4.0 software were employed to independently identify glandular and adipose tissues, reconstruct accurate three-dimensional anatomical models, and quantitatively analyze tissue volumes. Results: The MRI-based three-dimensional segmentation and reconstruction method accurately distinguished glandular and adipose tissues in male breasts, establishing precise three-dimensional anatomical models with excellent reproducibility and operational consistency. Among the 52 patients with bilateral gynecomastia, glandular tissue volume exhibited a markedly non-normal distribution, with a median of 6.11 cm³ (IQR, 3.03–12.98 cm³). Adipose tissue volume followed a normal distribution with a mean of 1348.84 ± 494.97 cm³. The total breast tissue volume also showed a normal distribution, with a mean of 1361.97 ± 496.83 cm³. The proportion of glandular tissue in total breast volume was non-normally distributed with a median of 0.50% (IQR, 0.27–1.21%), while the proportion of adipose tissue was also non-normally distributed with a median of 99.50% (IQR, 98.79–99.73%). Conclusions: MRI combined with computer-assisted three-dimensional segmentation and reconstruction technology efficiently and accurately achieves spatial identification, three-dimensional modeling, and volumetric quantification of glandular and adipose tissues in patients with bilateral gynecomastia. It objectively reveals the spatial compositional characteristics of male breast tissues. This approach provides precise, quantitative data for clinical decision-making regarding surgical treatment of gynecomastia, featuring robust standardization and strong clinical applicability. Full article

During the Gas Metal Arc Welding (GMAW) process, intense arc light and dense fumes cause local overexposure in RGB images and data loss in point clouds, which severely compromises the extraction accuracy of circular closed-curve weld seams. To address this challenge, this paper proposes a multimodal fusion method for weld seam extraction under arc light and fume interference. The method begins by constructing a weld seam edge feature extraction (WSEF) module based on a synergistic fusion network, which achieves precise localization of the weld contour by coupling image arc light-removal and semantic segmentation tasks. Subsequently, an image-to-point cloud mapping-guided Local Point Cloud Feature extraction (LPCF) module was designed, incorporating the Shuffle Attention mechanism to enhance robustness against noise and occlusion. Building upon this, a cross-modal attention-driven multimodal feature fusion (MFF) module integrates 2D edge features with 3D structural information to generate a spatially consistent and detail-rich fused point cloud. Finally, a hierarchical trajectory reconstruction and smoothing method is employed to achieve high-precision reconstruction of the closed weld seam path. The experimental results demonstrate that under severe arc light and fume interference, the proposed method achieves a Root Mean Square Error below 0.6 mm, a maximum error not exceeding 1.2 mm, and a processing time under 5 s. Its performance significantly surpasses that of existing methods, showcasing excellent accuracy and robustness. Full article

The aerodynamic performance of nacelle inlets under crosswind conditions is crucial for engine stability and efficiency. Current parametric investigations are predominantly focused on cruise operations, with minimal consideration given to crosswind conditions. This study employs an iCST-based parametric modeling approach to construct geometric models. A systematic examination of key geometric parameters—including the throat axial location, fan face radius, and leading-edge radii of the inner and outer contours is conducted. The reliability of the numerical methodology was established through a two-step validation process using both the iCST-generated non-axisymmetric model and the DLR-F6 benchmark model, followed by a geometric sensitivity analysis based on parametrically generated axisymmetric models. The results demonstrate that the inner contour leading-edge radius (ROC_I/R_hi) has the most substantial influence on flow separation. When ROC_I/R_hi decreases from 7.84% to 3.46%, the peak maximum circumferential total pressure distortion index (IDCmax) is increased by 86.78% with a 53.85% rearward shift in the complete reattachment mass flow rate. Correspondingly, a similar reduction in the outer contour leading-edge radius (ROC_O/R_hi) from 9.38% to 4.69% results in a 55.50% increase in peak IDCmax and a 33.33% rearward shift. Comparatively, the fan face radius shows minimal impact on flow distortion (increases by 9.72%), but more pronounced effects on total pressure recovery, while rearward movement of the throat axial location (35.00% to 69.00%) causes a 30.03% rise in IDCmax and 43.75% complete flow reattachment delay. It is concluded that the leading-edge optimization is crucial for crosswind resilience, with the inner contour geometry being particularly influential, providing parametric foundations for robust inlet design across a wide range of operating regimes. In addition, it is also found that the effects of Reynolds number (Re) lie in two folds: (1) For a fixed model scale, the aerodynamic performance of the inlet suffers a remarkable degradation with rapidly rising IDCmax as the crosswind velocity-based Re is increased to cause significant flow separations. (2) For a fixed crosswind velocity, the peak IDCmax progressively decreases with the increasing scale based Re, while σ exhibits an overall enhancement as Re rises. Full article

Background: Capsular contracture remains one of the most challenging complications of implant-based breast reconstruction, particularly in patients undergoing postmastectomy radiotherapy (PMRT). Autologous fat grafting has been proposed as a regenerative strategy to mitigate radiation-induced damage, but long-term data are limited. Methods: We retrospectively reviewed women who underwent two-stage implant-based breast reconstruction followed by PMRT (50 Gy in 25 fractions) between 2010 and 2021 at Ospedale Sant’Anna, Como. Eligible patients subsequently received at least one session of autologous fat grafting after radiotherapy. Primary outcome was the incidence and severity of capsular contracture; secondary outcomes included the need for salvage autologous reconstruction, oncologic safety, and patient-reported satisfaction. Results: Thirty-two patients met inclusion criteria. The mean age was 56.1 years, and mean BMI was 23.8 kg/m². All underwent submuscular two-stage reconstruction with anatomically shaped implants (mean volume 336 cc). Patients received an average of 1.7 fat grafting sessions (mean cumulative volume 180 cc). At a mean follow-up of 7.7 years, capsular contracture occurred in 6 patients (18.8%): 4 with Baker grade III and 2 with Baker grade II. No cases of severe (grade IV) contracture were observed. Importantly, no patient required salvage autologous reconstruction, and no local recurrences were recorded. Minor donor-site complications (transient edema or ecchymosis) occurred in 18.7% of patients. Subjective satisfaction was uniformly high, with reported improvements in breast softness and contour. Conclusions: Fat grafting appears to be a safe and effective adjunct in maintaining implant-based breast reconstruction after radiotherapy. In this long-term series, lipofilling was associated with a lower incidence of capsular contracture compared with historical rates, absence of severe contracture, and no oncologic events. For selected patients who are not candidates for autologous reconstruction, fat grafting may represent a valuable strategy to preserve implant viability, improve tissue quality, and reduce the need for salvage procedures. Full article

Background/Objectives: In plastic surgery, the administration of tranexamic acid (TXA) has gained increasing support by current literature, highlighting its relevance in clinical practice. This study evaluates the perioperative impact of prophylactic intravenous TXA administration in abdominoplasty, focusing on surgical outcome, drainage pattern, complications, and laboratory parameters (hematocrit/hemoglobin). Methods: This retrospective, single-center cohort study analyzed 58 abdominoplasties, which were divided into two groups: patients treated perioperatively with tranexamic acid for 48 h (TXA group; n = 24) and without TXA (no-TXA group; n = 34). Results: Patients in the TXA group had a significantly shorter length of hospital stay (p = 0.008) and a lower postoperative daily drainage volume on postoperative days: 3 (p = 0.047), 4 (p = 0.011), 7 (p = 0.014), 8 (p = 0.024), and 9 (p = 0.042). The overall complication rate was also significantly reduced with TXA (25.0% vs. 52.9% in the no-TXA group; p = 0.033). Postoperative declines in hematocrit and hemoglobin were less pronounced in the TXA group (p = 0.353 and p = 0.255, respectively). Furthermore, the intravenous administration of TXA was well tolerated, and no associated thromboembolic events were observed. Conclusions: Intravenous TXA appears to reduce complications, drainage volumes, and hospital stay in abdominoplasty patients, while being safe and well tolerated. Although further studies are needed to define optimal dosing, administration protocols, and long-term safety, these findings support the potential benefits of TXA for both patients and healthcare systems, thereby enabling a standardized approach to body contouring surgery. Full article

Creating the most accurate numerical models with the same dynamic behavior as real structures plays an important role in the development process of various facilities. This article deals with the use of experimental methods, particularly experimental modal analysis (EMA), scanning, detection, spectral analysis, and mechanical testing in combination with the optimization techniques of the ANSYS 2024 R1 software to calibrate numerical models of axisymmetric structures. The proposed methodology was tested on a steel pipe whose geometric and material properties were both available. Within the updating of finite element models (FEMU) with one or two design variables, the influence of the range of feasible values on the accuracy of the observed parameters was examined. The updating process led to the acquisition of such a pipe model, which natural frequencies differed by less than 1.5% from the results estimated in EMA, and its weight also differed only minimally. The proposed methodology was then used for the FEMU of a pressure vessel whose contour was obtained by a 3D scanning method; material properties were investigated, and all wall thicknesses, i.e., eleven design variables, were unknown and thus determined by an iterative optimization technique. Using the Multi-Objective Genetic Algorithm (MOGA) method, the dimensions of the vessel were first updated for their shell model and subsequently for the 3D model. The resulting natural frequencies of the model with applied internal pressures of 0 bar, 40 bar, and 80 bar differed from those estimated experimentally by less than 1.2%. Full article

Unmanned aerial vehicle (UAV) remote sensing is critical in assessing post-earthquake building damage. However, intelligent disaster assessment via remote sensing faces formidable challenges from complex backgrounds, substantial scale variations in targets, and diverse spatial disaster dynamics. To address these issues, we propose UEBNet, a high-precision post-earthquake building instance segmentation model that systematically enhances damage recognition by integrating three key modules. Firstly, the Depthwise Separable Convolutional Block Attention Module suppresses background noise that visually resembles damaged structures. This is achieved by expanding the receptive field using multi-scale pooling and dilated convolutions. Secondly, the Multi-feature Fusion Module generates scale-robust feature representations for damaged buildings with significant size differences by processing feature streams from different receptive fields in parallel. Finally, the Adaptive Multi-Scale Interaction Module accurately reconstructs the irregular contours of damaged buildings through an advanced feature alignment mechanism. Extensive experiments were conducted using UAV imagery collected after the Ms 6.8 earthquake in Tingri County, Tibet Autonomous Region, China, on 7 January 2025, and the Ms 6.2 earthquake in Jishishan County, Gansu Province, China, on 18 December 2023. Results indicate that UEBNet enhances segmentation mean Average Precision (${mAP}^{seg}$) and bounding box mean Average Precision (${mAP}^{box}$) by 3.09% and 2.20%, respectively, with equivalent improvements of 2.65% in F1-score and 1.54% in overall accuracy, outperforming state-of-the-art instance segmentation models. These results demonstrate the effectiveness and reliability of UEBNet in accurately segmenting earthquake-damaged buildings in complex post-disaster scenarios, offering valuable support for emergency response and disaster relief. Full article

The objective of this research endeavor is to present engineering data pertaining to the size and shape characteristics of strawberries, which have a wide range of applications in industry, and to obtain the data necessary for the development and design of product processing systems. In this study, standard strawberry varieties were utilized, and analyses were conducted by means of an image-processing method. The projection area (601.5–762.0 mm²), length (34.0 mm), width (28.6 mm) and surface area (28.6 cm²) of the strawberry samples were measured in the horizontal and vertical orientation, in order to ascertain their size characteristics. Furthermore, the sphericity (86.1%) and roundness (1.039–1.087) parameters were calculated for the shape characteristics, accordingly. The findings of the correlation analysis suggested that the size parameters of the fruits exerted no influence on fruit shape characteristics. In the elliptic Fourier analysis performed to reveal the shape differences in the fruit, the contour geometry of each fruit sample was extracted, the principal component (PC) scores describing the shape were obtained and the shape categories of the fruit were determined. Following the analysis of the PCs, it was determined that 90.77% of the total shape variance was explained by the first seven components. Consequently, the shape of the strawberry fruit was defined as a spherical cone. Following the implementation of a discriminant analysis in conjunction with a clustering process, which categorized the samples into seven distinct shape categories employing the k-means algorithm, an accuracy rate of 94.1% was achieved. Full article