Search Results (416)

Land degradation and declining groundwater availability threaten the sustainability of rural livelihoods across semi-arid regions. This study evaluates the hydrological performance of contour-based trenches as a low-cost and replicable nature-based solution (Nbs) for soil restoration, runoff regulation, and potential distributed managed aquifer recharge (MAR) in Guerrero, Mexico. The structures were installed on 12% slopes and designed using a simplified water balance criterion based on trench storage capacity, runoff coefficient, and representative rainfall events. Each trench was constructed along contour lines with overflow notches and connecting micro-trenches to improve hydraulic continuity, reduce erosion, and enhance infiltration opportunities under degraded field conditions. After one year of field monitoring, the trenches reached an average filling efficiency of approximately 90% per effective rainfall event, with estimated infiltration rates ranging from 0.0069 to 0.011 L·s⁻¹. Soil moisture in the upper soil layer showed a relative increase of approximately 10–18% compared to adjacent untreated areas, while visible reductions in runoff velocity, sediment transport, and surface erosion were observed across the treated plot. Based on trench storage capacity, observed infiltration behavior, and assumed deep percolation fractions, the potential induced recharge was estimated between 216 and 360 m³·yr⁻¹ (43–72 mm·yr⁻¹). These values represent indicative plot-scale estimates rather than direct measurements of aquifer recharge, since no tracer studies or piezometric validation were performed. The results demonstrate that contour-based trenches contribute not only to infiltration enhancement and runoff control, but also to short-term soil restoration and improved water availability in rainfed agricultural systems. Their low-cost implementation, combined with community-based maintenance and adaptation to local environmental conditions, makes them a viable complementary strategy for strengthening decentralized water management, soil resilience, and climate adaptation in semi-arid rural landscapes. However, long-term effectiveness remains dependent on maintenance continuity, institutional support, and local governance conditions. Further multi-year monitoring and direct hydrogeological validation are recommended to improve the design and replicability of decentralized MAR systems. Full article

In high-speed machining, linear toolpaths constructed from a series of short line segments are widely used but inevitably introduce tangent and curvature discontinuities at segment junctions, which may cause feedrate fluctuation and contouring error. To address this problem, this study proposes a real-time corner smoothing and feedrate interpolation method based on dual cubic Bézier transition curves and an optimal error assignment model. The main contribution lies in coupling analytical corner rounding with error allocation: the approximation error and maximum curvature of the transition curves are obtained explicitly, while the allowable tolerance is optimally distributed between approximation error and chord error so that the overall trajectory error remains within the prescribed bound. A jerk-limited look-ahead interpolator is then developed through reverse scanning and forward interpolation to satisfy geometric constraints, drive constraints, and feedrate commands. Simulation results for a three-dimensional toolpath show that the approximation error, chord error, and total trajectory error are all constrained within the preset tolerance of 0.05 mm. In the mask-machining case, the proposed method reduces the machining time to 13.9 s, corresponding to reductions of approximately 70% and 25% compared with the method without look-ahead and the method with look-ahead only, respectively. These results indicate that the proposed framework can improve motion smoothness and machining efficiency while maintaining trajectory accuracy. Full article

This paper addresses the problem of counting stacked components in industrial scenarios and proposes a method that combines close-range scanning for complete contour acquisition with deep learning for quantity recognition: The contour acquisition system consists of a line array camera and a linear laser. Both are arranged horizontally at a certain angle, and the laser line is perpendicular and in the same direction as the stacking of the components. The system scans and connects single-row pixels along the stacking direction to obtain the contour. This method effectively avoids the occlusion problem caused by uneven stacking of components. The quantity recognition algorithm adopts a network structure similar to Encoding–Decoding using the component gap (cls: 0 indicates not, 1 indicates yes) and the endpoint coordinates of the separation line segment [cls, x1, y1, x2, y2] to form a label. Multi-scale anchors are introduced to predict the translation distance of the line segment (positive or negative, indicating direction). The prediction head is fully convolutional, and the loss for regression is computed using the predicted endpoints of the ground-truth line segments. A line segment redundancy removal method is proposed to output the predicted confidence (conf) and coordinates [conf, px1, py1, px2, py2] for each component gap. The self-built dataset is used for training and validation. Experiments show that the recognition accuracy of each image reaches 95.79%, and the gap recognition accuracy reaches 99.62%, which can meet the requirements of automation. Full article

Automatic classification of anime character painting styles is of great significance to the digital cultural industry and visual content production. Existing methods are prone to shortcut learning when handling complex color rendering and cannot fully decouple high-frequency line drafts from low-frequency colors. To solve this problem, this study proposes an improved deep learning classification method based on EfficientNetV2-B0. This method introduces random amplitude scaling (RAS) at the data input terminal. It realizes effective decoupling of colors and line-draft structures through random low-frequency amplitude perturbation, and suppresses the model’s excessive dependence on global color information from the source. Edge-guided coordinate attention (EG-CA) is integrated into the backbone network. It enhances the perception of line and contour features through edge weights and improves the model’s ability to capture fine-grained structural features. Adaptive scale feature aggregation (ASFA) is designed in the multi-scale feature fusion stage. It achieves efficient fusion of shallow textures and deep semantics through dynamic weighting, so as to enhance the model’s discriminative ability under complex painting styles. On a dataset containing 7887 images of four categories, the classification accuracy of the model reaches 95.81%. It significantly outperforms mainstream models such as MViTv2-T. Meanwhile, the number of parameters is only 7.84 M and the inference speed reaches 68.83 FPS. Ablation experiments show that the synergistic effect of the three modules improves the accuracy of the baseline model by 6.06%. It proves that the proposed method provides reliable technical support for the structured management and copyright traceability of anime images. Full article

Schistosomiasis (bilharzia) is a parasitic infection caused by trematodes of the Schistosoma genus and remains a significant health burden in endemic regions. Granulomatous host responses to deposited Schistosoma eggs in small veins and tissues result in progressive changes and characteristic imaging findings. This diagnostic radiological review synthesizes the published literature and highlights key and robust imaging findings that facilitate the diagnosis of Schistosoma mansoni and Schistosoma haematobium, with emphasis on modality-specific patterns and disease staging. Schistosoma mansoni primarily affects the liver, causing periportal fibrosis visible as “pipe-stem” echogenic thickening upon ultrasonography, which may progress to portal hypertension and chronic liver disease. Liver cirrhosis is the end-stage disease manifested as an irregular liver contour with surface nodularity and lobar redistribution as right lobe atrophy with left and/or caudate lobe hypertrophy. Schistosoma haematobium predominantly affects the genitourinary system, causing urinary bladder wall thickening and calcification. Early disease, within three months of infection, may present with fine calcification, firstly in the bladder base and then extending to the whole bladder and even to the ureters. Calcification appears as a line or two parallel lines on radiography and as a circle in axial computed tomography (CT) images, which is pathognomonic for early-stage Schistosomiasis. In contrast studies, including conventional urography and CT urography, Schistosoma eggs appear as bubble-like filling defects in the ureter, kidney, and bladder, manifested as ureteritis, pyelitis, and cystitis cystica. Late stages appear as coarse calcification, fibrosis, strictures, and reduced bladder capacity and are associated with an increased risk of bladder squamous cell carcinoma. Moreover, Schistosomiasis calcification can present in genital organs, especially in the seminal vesicles; in the prostate in males; and in the vulva, cervix, and perineum in females. Ultimately, Schistosoma mansoni and haematobium eggs can reach the spinal cord, leading to acute myelopathy with paraparesis, urinary retention, or paraplegia. Recognition of characteristic imaging patterns of Schistosomiasis is essential for early diagnosis, accurate staging, and prevention of long-term complications. Full article

This study systematically optimized the geometric parameters of three typical cross-sections for curved-wall highway tunnel inner contours. Aiming to minimize the net excavation area, a unified genetic algorithm-based optimization framework was established for systematic comparison of four typical curved-wall section types and implemented on the Matlab(R2023b) platform, incorporating encoding, selection, crossover, and mutation operations for global optimization of geometric parameters across different section types. The optimized sections were further validated for structural performance using Midas GTS NX. Results show that the proposed multi-type optimization framework effectively reduced tunnel excavation areas across all section types, with mean optimization rates of 2.60% ± 0.21%, 2.11% ± 0.03%, 4.70% ± 0.02%, and 2.54% ± 0.02% (95% CI) achieved for single-circle, triple-circle Model-1, triple-circle Model-2, and five-circle sections, respectively, providing quantitative evidence for section-type selection in highway tunnel design. In terms of structural performance, the optimized sections demonstrated favorable axial force and bending moment characteristics. The findings provide a quantitative basis combining economic efficiency and structural rationality for tunnel section design, offering significant engineering application value and technical support for standardized and refined highway tunnel design in China. Full article

Integrated die-cast components reduce machining/assembly steps and improve mechanical dynamic characteristics, eliminating joint loosening/fracture risks after long-term use. However, the highly variable geometries and random spatial distributions of burrs, flash, parting lines, and risers in castings invalidate pre-programmed or teach-in robotic grinding methods. This paper reviews recent progress and future trends in robotic grinding, analyzing four core aspects: force control stability/adaptability (e.g., adaptive impedance control can reduce average force-tracking error to 0.38 N), trajectory planning/path generation (e.g., error-driven compensation can lower contour error by 34.2–55.1%), process parameter optimization, and challenges of sensing latency/quality evaluation (e.g., deep learning models achieve 97.64% accuracy in identifying abrasive belt wear states). The key enabling technologies are summarized, including active/passive compliant force control, model-/data-driven adaptive trajectory planning, intelligent process parameter optimization integrating physical mechanisms and data-driven approaches, and multi-modal state monitoring with online quality assessment. Representative applications (metal castings, aero-engine blades, thin-walled components, weld seams) are presented, and prospective research directions are proposed. This paper provides a comprehensive reference for theoretical research and engineering practice in this field. Full article

The acquisition of spaceborne tomographic data usually requires a longer period of time due to the satellite’s long revisit period. To address this issue, it is possible to leverage the similarity of neighboring pixels in order to perform tomographic reconstruction of building targets in urban areas. As a result, the insufficient number of samples can be approximately substituted by pixels with similar scattering characteristics. However, the current utilization of building structures is often limited to horizontal characteristics such as contour lines (CL); in addition, extraction methods either rely on external data as prior information or are constrained by the need to fit operations, which limits the shape of the contour lines. This paper proposes a spaceborne tomographic reconstruction method based on building characteristics, starting from data and fully utilizing the horizontal and vertical characteristics of buildings for reconstruction. First, interferometric information is used to assist in tomographic processing and a strategy combining multi-point growth with multi-level fusion is employed to extract contour lines. Additionally, the vertical characteristics of buildings are established to provide constraints on the solution space for tomographic processing. The three-dimensional reconstruction of isolated and vertical buildings is then achieved by combining signal elimination techniques. By more fully exploiting the structural characteristics of buildings, the proposed method is capable of recovering building structures even with a limited number of samples. The effectiveness of the proposed method is validated through simulated data and TerraSAR-X data. Full article

Winter pruning is a key management practice in viticulture that directly affects vine architecture, yield balance, and grape quality. At the same time, it is a highly labor-intensive operation, and the selective identification of appropriate cutting locations remains one of the main challenges limiting the automation of pruning in vineyards. Advances in machine vision provide new opportunities to support the development of robotic pruning systems. The objective of this study was to develop and evaluate a vision-based method for estimating grapevine pruning points and cutting lines using instance segmentation outputs generated by YOLO models. A dataset of 1500 RGB images of dormant grapevines was collected under field conditions in the Nobilis vineyard located in southeastern Poland. Two annotation strategies were implemented to define pruning regions. YOLO-based instance segmentation models were trained and evaluated for detecting cutting-related structures. Based on the predicted segmentation masks, a geometry-based method termed PCAcutSeg-V was developed to estimate class-dependent cutting points and cutting lines using principal component analysis applied to object contours. The results indicate that YOLOv8 and YOLO11 architectures achieved the highest segmentation performance among the evaluated models. The simplified annotation strategy provided more stable geometric inputs for the PCAcutSeg-V method, enabling more reliable estimation of cutting points and cutting lines compared with the extended annotation approach. When combined with the PCAcutSeg-V method, the proposed perception–geometry pipeline achieved high effectiveness in pruning decision estimation. The method was further implemented in a real-time processing pipeline using an RGB camera and an edge computing platform, where it maintained performance consistent with the results obtained from offline image analysis. These findings demonstrate that combining deep learning-based instance segmentation with deterministic geometric reasoning enables accurate and interpretable estimation of grapevine pruning locations and provides a promising foundation for future autonomous pruning systems. Full article

Flow lines in aluminum alloy forgings are not merely post-deformation metallographic features; they are integrated indicators of material transport, microstructural evolution, defect susceptibility, and service performance. This review critically examines the mechanisms controlling flow-line evolution, with emphasis on constitutive flow behavior, dynamic recovery and recrystallization, second-phase redistribution, friction, thermal gradients, and die/preform design. It then evaluates how abnormal flow paths promote key defects, including folding/laps, flow-through discontinuities, vortex-like instability, and exposed flow lines, and distinguishes well-established mechanisms from topics that still rely on indirect evidence. Particular attention is given to the effects of flow-line morphology on anisotropy, notch sensitivity, corrosion-assisted damage, and fatigue life in forged aluminum alloys. Current control strategies, including preform optimization, FE-based backward tracing, multiphysics defect indices, frictional heat management, and isothermal forging, are also assessed. The available literature shows that stable contour-following flow lines are essential for the simultaneous control of defect formation, microstructural homogeneity, and durability, while major research needs remain in in situ validation, quantitative defect criteria, and digitally closed-loop process control. This review is therefore framed as a critical narrative synthesis rather than a formal systematic review; emphasis is placed on forging-centered studies that directly relate flow-path evolution to defect formation, anisotropy, fatigue, and process optimization, while evidence transferred from adjacent processes is treated as mechanistic support rather than equivalent proof. Full article

Clothing pattern-making is an important part of transforming design concepts into finished products; however, the traditional manual pattern-making process is not only time-consuming, but also suffers from inefficiency, which seriously restricts the automation and precision of clothing production. This study proposes an automated clothing pattern-making method, the core of which lies in the organic combination of an improved Pix2Pix model and a conditional diffusion model. The improved Pix2Pix model effectively captures the complex structural information in clothing patterns by introducing a multi-scale discriminator and a new composite loss function. Due to limited data, the improved Pix2Pix falls short in terms of image generation quality, so a conditional diffusion model was introduced to enhance the detail and overall integrity of the generated images. Experiments were conducted on pattern-making tasks for the sleeves and back panels of various typical clothing styles. The sleeve components primarily validated the model’s basic generation capabilities. The results showed that the improved Pix2Pix-generated initial template could capture the basic contour structure, and after diffusion model repair, the lines became clearer and the details more complete; the back panels components validated the model’s robustness. Quantitative results showed that the proposed method achieved SSIM, PSNR, and LPIPS values of 0.869, 22.31, and 0.1318, respectively. Compared with the results of other advanced models, the proposed method exhibits the highest accuracy and clarity in the generated images, confirming its practicality and effectiveness in automated apparel pattern-making. Full article

In the fabrication process of pipelines for petrochemical and other industries, weld reinforcement is often excessive and adversely affects subsequent processes such as anticorrosion treatment and surface coating. Weld reinforcement must be removed through a grinding process. Welding deformation and fit-up errors often lead to highly irregular weld geometries, which makes robotic grinding difficult and causes the task to still heavily rely on manual operation. To address this issue, this study proposes an automatic weld recognition and grinding trajectory planning method based on 3D visualization and deep learning. A weld recognition network, termed WSR-Net, has been developed based on an improved PointNet++ architecture with a cross-attention mechanism, achieving a segmentation accuracy of 98.87% and a mean intersection over union of 90.71% on the test set. An intrinsic shape signature (ISS) key point selection algorithm with orthogonal slicing-based pruning optimization is developed to robustly extract key weld ridge points that characterize the weld trend on rugged weld surfaces. According to the height differences between the weld and the adjacent base metal surfaces, the grinding reference surface is fitted using the weld contour through the moving least-squares method. The ridge line points are projected onto the grinding reference surface along the local normal to generate the expected grinding trajectory points. The grinding trajectory that meets the process constraints is generated through reverse layer slicing. Grinding experiments demonstrate that the proposed WSR-Net achieves robust segmentation performance for both planar and curved surface welds. With the reverse layered trajectory planning method, the proposed method enables high-precision automatic grinding of complex spatially curved surface welds. The results show that the final grinding mean error is 0.316 mm, which satisfies the preprocessing requirements for subsequent processes. The proposed method provides a feasible technical method for the intelligent grinding of spatially curved surface welds. Full article

Background/Objectives: The biologically oriented preparation technique (BOPT) is a vertical tooth preparation approach that eliminates a conventional finish line and positions the prosthetic margin within the gingival sulcus, aiming to promote peri-restorative soft tissue adaptation through controlled gingival remodeling. This article describes a clinical case report of a hybrid impression protocol combined with a digital workflow intended to address some of the main clinical limitations of BOPT, particularly the recording of deep subgingival margins and the transfer of the emergence profile from the provisional to the definitive restoration. Methods: The proposed technique combined a conventional silicone impression to obtain a complete reading of the gingival sulcus with intraoral digital scanning, complemented by extraoral scanning of the provisional restoration to reproduce its subgingival morphology within the definitive prosthetic workflow. Results: Within the limitations of a single clinical case with short-term follow-up, this hybrid approach showed a satisfactory esthetic outcome and favorable short-term peri-coronal soft tissue behavior. Conclusions: This hybrid workflow may represent a feasible clinical option for transferring the cervical contour and emergence profile to the definitive prosthesis in anterior BOPT restorations. Full article

To address critical issues in traditional quality control methods for discrete Euler solutions in underwater magnetic target detection—such as excessive filtering of valid solutions during divergence suppression, parameter settings reliant on subjective experience, and insufficient noise resistance—this study proposes a novel approach combining the Artificial Protozoa Optimizer (APO) with DBSCAN clustering. Based on the distribution characteristics of Euler solutions, an optimization objective function incorporating Euler solution residual penalty terms and contour line coefficients was constructed. The APO algorithm identifies DBSCAN clustering parameters that minimize this objective function, thereby enhancing clustering precision and accuracy. This method selects optimal Euler solution sets, enabling high-precision localization of magnetic targets. Simulation and field test results demonstrate that compared to statistical screening methods, the optimized algorithm achieves a 52.52% and 76.33% increase in the retention rate of valid solutions for noise-free and noisy data, respectively, while reducing the retention rate of invalid solutions by 28.57% and 94.21%. In field data, the average deviation from the true center of gravity is reduced by 28.06%. Full article

Background: Floating barbed threads are commonly used for minimally invasive midface lifting and rely on mobile subcutaneous tissue for support, which may limit stability. Fixation is primarily achieved by barb engagement within the subcutaneous fat and fibrous septa of the retinacula cutis. Objectives: To describe an anatomically guided modification of the APTOS Excellence Visage Soft (PLLA/PCL) thread technique, positioning the terminal segment posterior to the zygomatic retaining ligament line with the aim to enhancing mechanical stability. This technical report presents the anatomical rationale, procedural steps, and illustrative clinical cases demonstrating feasibility. Methods: The modified technique uses a single-entry point at the superior zygomatic margin, with five threads per hemiface. After linear insertion, the cannula is rotated laterally and inferiorly to position the terminal barbs posterior to the zygomatic retaining ligament line, thereby transferring tensile load toward a more fixed anatomical structure. Representative cases were documented and are presented. Results: Illustrative cases showed immediate midface elevation with improved malar projection and softening of the nasolabial and mentolabial folds. Standardized 3D imaging and vector analysis demonstrated a superolateral pattern of soft tissue displacement along the intended vectors, consistent with the proposed fixed-anchorage concept. The procedure was well tolerated, with only mild and transient local effects observed. One illustrative case included photographic follow-up at 12 months, in which preservation of midface contour and malar projection was visually appreciable. Conclusions: Redirecting the terminal thread segment posterior to the zygomatic retaining ligament line is a feasible modification that may contribute to improved vector stability by engaging a fixed fascial structure. Observations—including one case with 12-month follow-up—support the anatomical plausibility of the approach, although controlled studies with objective endpoints are necessary to confirm long-term efficacy and reproducibility. Full article