Morphological Edge Research Articles

Carbon Sequestration and Landscape Influences in Urban Greenspace Coverage Variability: A High-Resolution Remote Sensing Study in Luohe, China

Urbanization has significantly altered urban landscape patterns, leading to a continuous reduction in the proportion of green spaces. As critical carbon sinks in urban carbon cycles, urban green spaces play an indispensable role in mitigating climate change. This study aims to evaluate the carbon capture and storage potential of urban green spaces in Luohe, China, and identify the landscape factors influencing carbon sequestration. The research combines on-site data collection with high-resolution remote sensing, utilizing the i-Tree Eco model to estimate carbon sequestration rates across areas with varying levels of greenery. The study reveals that the carbon sequestration capacity of urban green spaces in Luohe City is 1.30 t·C·ha−1·yr−1. Among various vegetation indices, the Enhanced Vegetation Index (EVI) explains urban green space carbon sequestration most effectively through an exponential model (R2 = 0.65, AIC = 136.5). At the city-wide scale, areas with higher greening rates, better connectivity, and more complex edge morphology exhibit superior carbon sequestration efficiency. The explanatory power of key landscape indices on carbon sequestration is 78% across the study area, with variations of 71.5%, 62%, and 84.9% for low, medium, and high greening rate areas, respectively. Moreover, when greening rates reach a certain threshold, maintaining and optimizing the quality of existing green spaces becomes more critical than simply expanding the green area. These insights provide valuable guidance for urban planners and policymakers on enhancing the ecological functions of urban green spaces during urban development.

Rotor proliferation promotes high-brightness AIE of iridium emitter accomplishing high-contrast luminous imaging of latent fingerprints to level 3 details

Luminous imaging of latent fingerprints (LFPs) necessitates the possession of high-brightness aggregation-state luminescence by developers to ensure sufficient imaging contrast and resolution. A novel strategy involving incremental rotor modification is presented for AIE activation of the iridium developer. The rotor proliferation prominently improves the rotational activity of groups and facilitates high-efficiency RIM, thereby prompting the AIE activation of iridium developer with high luminous efficiency. Subsequently, a prompt, high-contrast, and robust LFP imaging protocol is developed utilizing the high-brightness AIE-active iridium developer. This innovative protocol realizes the luminous imaging and quantification of microscopic features in fingerprint ridges and furrows, including ridge widths, edge morphology of ridges, included angles, pores, and pore pitches with exceptional imaging contrast and refined detail resolution. Moreover, it allows for accurate identification of individual traits across diverse substrates without any pre-/post-processing to LFPs. The high-brightness AIE-active iridium developer provides outstanding aging resistance to developed fingerprints, thereby strongly supporting the acquisition, transfer, and preservation of fingerprint evidence. The luminous imaging protocol of LFPs based on high-brightness AIE exhibits robust adaptability to actual scenes and offers a premium scheme for facilitating forensic investigation.

Edge morphology attention mechanism and optimal geometric matching connection model for vascular segmentation

Over the past decades, medical image segmentation methods have been intensively developed, but there are still a number of unsolved challenging problems in vascular image segmentation, including broken vessels, insufficient vessel branches, and missing small vessels. In order to optimize the topology and accuracy of segmented vessels, we propose a novel Edge Morphology Attention Network (EMA-Net) for the segmentation of vessel-like structures, and an Optimal Geometric Matching Connection (OGMC) model to connect the broken vessel fragments. The EMA-Net has an edge attention module that is able to improve segmented performances of edges and small branches by morphology operation extracting boundary voxels on multi-scales. The OGMC model uses the geometry concept of curve touching to filter out fragmented vessel endpoints, and then employs minimal surfaces to determine the optimal connection order between blood vessels. Finally, we adopt geodesics to repair missing vessels under a Riemannian metric. Our method achieves superior or competitive results compared to state-of-the-art methods on four datasets of 3D vascular segmentation tasks, both effectively reducing vessel broken and increasing vessel branch richness, yielding blood vessels with more precise topological structures.

Patterning damage mechanisms for two-dimensional crystalline polymers and evaluation for a conjugated imine-based polymer

High-quality patterning determines the properties of patterned emerging two-dimensional (2D) conjugated polymers and is essential for potential applications in future electronic nanodevices. However, the most suitable patterning method for 2D polymers has yet to be determined because we still do not have a comprehensive understanding of their damage mechanisms by visualizing the structural modification that occurs during the patterning process. Here, the damage mechanisms during patterning of 2D polymers, induced by various patterning methods, are unveiled based on a systematic study of structural damage and edge morphology in an imine-based 2D polymer (polyimine). Patterning using a focused electron beam, focused ion beam (FIB) and mechanical carving is evaluated. The focused electron beam successively introduces a sputtering effect, knock-on displacement damage and massive radiolysis with increasing electron dose from9.46×107electrons nm-2to1.14×1010electrons nm-2. Successful patterning is enabled by knock-on damage but impeded by carbon contamination beyond a critical sample thickness. A FIB creates current-dependent edge morphologies and extensive damage from ion implantation caused by the tail of the unfocused beam. A precisely controlled tip can tear the polyimine film through grain boundaries and hence create a patterning edge with suitable edge roughness for certain application scenarios when beam damage is avoided. Taking structural damage and the resulting quantitative edge roughness into consideration, this study provides a detailed instruction on the proper patterning techniques for 2D crystalline polymers and paves the way for tailored intrinsic properties and device fabrication using these novel materials.

Achieving high geometric fidelity in vat photopolymerization additive manufacturing through liquid surface support

The top-down vat photopolymerization (VPP) printing process, known for its extensive material adaptability and elimination of the release step between the printed object and the separation film, is widely used in 3D printing of advanced functional materials. However, for slurries that cannot achieve rapid self-leveling, edge morphology distortion in printed objects reduces geometric precision. This study investigated the issue of edge morphology distortion in the top-down VPP printing process, and proposes a liquid surface support (LSS) printing method that can be applied to general VPP printers. The results show that the protective slurry fence printed simultaneously with the object can provide excellent liquid surface support for the uncured layer above the printed object, which shifts the edge morphology distortion issue from the target object to the fence. The gap distance between the fence and the target sample is identified as the primary factor affecting edge morphology fidelity, which is influenced by surface tension. Finally, we successfully validated the application of the LSS printing method in the printing of ceramic dental prostheses and slender rod structures. This novel method significantly enhances the geometric precision of printed samples and is expected to promote the advancement of VPP printing applications for advanced functional materials.

Quantitative assessment method of thermal cycle accumulation mechanism during the pulsed laser single-channel multilayer cladding process

Pulsed laser cladding provides a periodical thermal accumulation, enabling a greater temperature gradient and cooling rate inside the substrate, which can efficiently ameliorate the microstructure of the cladding layer. It is crucial to quantitatively investigate the mechanism of the interaction for the cladding process parameters on the transient evolution of the multi-field coupling to improve the cladding quality. In this paper, a 3-D model during the single-channel multilayer pulsed laser cladding process was constructed which is grounded on the temperature-variable physical properties parameters of the material. The integrated consideration in the masking impact of the molten powder waist beam on the pulsed laser beam, the molten pool liquid metal surface tension, the influence of buoyancy on the flow, and the transient evolution of the molten pool edge morphology. The temperature, flow, and stress fields of the single-channel multilayer cladding process were computed and resolved. The mechanism in multi-field coupling impact of laser pulse frequency, laser power, and pulse duty cycle on the single-channel multilayer cladding process was emphasized. The response surface equations between each influencing factor and the cladding temperature, temperature change gradient, and thermal stress were determined according to the response surface method, and the sensitivity of each parameter was attained from the Monte Carlo method. Using SEM to observe the profile and microstructure of the cladding layer, the material hardness distribution was measured experimentally, and the effectiveness of the model was confirmed. This research supplies an essential rationale for upgrading the quality of pulsed laser cladding layers.

Improving zirconia ceramics grinding surface integrity through innovative laser bionic surface texturing

Zirconia ceramics have become an important material for the manufacture of key components in aerospace, military, energy, and other fields due to their excellent mechanical properties. However, their inherent high hardness and brittleness make them difficult to machine with high efficiency and low damage, resulting in poor surface integrity. Inspired by the ribbed groove structure of shark skin and the fractal structure of insect wing veins, two zirconia ceramics with different bionic textures are designed in this paper, and zirconia ceramic surfaces are bionically textured by using an ultrafast picosecond pulse laser. The effects of the special bionic textures on the grinding behavior of zirconia ceramics, specifically surface morphology, surface roughness, edge morphology, and subsurface damage, were investigated. The experimental results show that the surface integrity of the bionic textured zirconia ceramics is better compared to the untextured surface. In particular, the bionic insect wing veins texture exhibits the best surface quality with the least sub-surface damage, and the surface roughness Sa is reduced by a maximum of 21.46 %, and Sz is reduced by a maximum of 18.87 %. However, the best edge morphology is exhibited by the bionic shark skin texture. Additionally, bionic texturing improves grinding lubrication and cooling conditions. The results confirm that applying bionic texturing to zirconia ceramics effectively enhances grinding performance and improves grinding surface integrity.

Object tracking method based on edge detection and morphology

With the continuous development of science and technology, intelligent surveillance technology using image processing and computer vision is also progressing. To improve the performance of target detection and tracking, an improved target tracking method is proposed, which uses a combination of the Canny operator and morphology for the detection part, and a Kalman filter extended Kernel Correlation Filter (KCF) tracking algorithm approach for the tracking part. First, a convolution kernel of 3×3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$3\ imes 3$$\\end{document} is improved to a convolution kernel of 2×2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$2\ imes 2$$\\end{document} in the traditional Canny algorithm, and the pixel gradient in the diagonal direction is increased. Secondly, a mathematical morphology theory of nonlinear filtering is applied to the Canny edge detection algorithm, and this method effectively improves the clarity of image edges. Finally, the extended kernel correlation filtering algorithm is applied to video surveillance and Online Object Tracking Benckmark2013 (OTB2013) datasets for testing. The experimental results show that the method proposed in this paper can accurately detect moving targets and the algorithm has good accuracy and success rate.

A Practical Deep Learning Architecture for Large-Area Solid Wastes Monitoring Based on UAV Imagery

The development of global urbanization has brought about a significant amount of solid waste. These untreated wastes may be dumped in any corner, causing serious pollution to the environment. Thus, it is necessary to accurately obtain their distribution locations and detailed edge information. In this study, a practical deep learning network for recognizing solid waste piles over extensive areas using unmanned aerial vehicle (UAV) imagery has been proposed and verified. Firstly, a high-resolution dataset serving to solid waste detection was created based on UAV aerial data. Then, a dual-branch solid waste semantic segmentation model was constructed to address the characteristics of the integration of solid waste distribution with the environment and the irregular edge morphology. The Context feature branch is responsible for extracting high-level semantic features, while the Spatial feature branch is designed to capture fine-grained spatial details. After information fusion, the model obtained more comprehensive feature representation and segmentation ability. The effectiveness of the improvement was verified through ablation experiments and compared with 13 commonly used semantic segmentation models, demonstrating the advantages of the method in solid waste segmentation tasks, with an overall accuracy of over 94%, and a recall rate of 88.6%—much better than the best performing baselines. Finally, a spatial distribution map of solid waste over Jiaxing district, China was generated by the model inference, which assisted the environmental protection department in completing environmental management. The proposed method provides a feasible approach for the accurately monitoring of solid waste, so as to provide policy support for environmental protection.

Engineering of edge nitrogen dopant in carbon nanosheet framework for fast and stable potassium-ion storage

Amorphous carbons are promising candidates as the anode materials for potassium-ion hybrid capacitors (PIHCs). The insufficient storage sites and inferior diffusion kinetics limit their potassium-ion storage capability. Edge nitrogen and morphology engineering are effective pathways to construct accessible active sites and enhanced diffusion kinetics. However, the organic integration of both pathways in amorphous carbon is still challenging. Herein, a “twice-cooking” strategy, including two-step carbonization processes at 700 °C, is designed to synthesize edge-nitrogen-rich lignin-derived carbon nanosheet framework (EN-LCNF). In the first-step carbonization process, the staged gas releases of CO and CO2 from CaC2O4 decomposition exfoliate the carbon matrix into a carbon nanosheet framework. In the second-step carbonization process, the generated CaO reacts with the cyanamide units of graphitic carbon nitride (g-C3N4) to form an edge-nitrogen-rich framework, which is then integrated into the meso-/macropores of carbon nanosheet framework through sp3-hybridized C–N bonds. EN-LCNF with a high edge-nitrogen level of 7.0 at.% delivers an excellent capacity of 310.3 mAh g−1 at 50 mA g−1, a robust rate capability of 126.4 mAh g−1 at 5000 mA g−1, and long cycle life. The as-assembled PIHCs based on EN-LCNF anode and commercial activated carbon cathode show a high energy density of 110.8 Wh kg−1 at a power density of 100 W kg−1 and excellent capacitance retention of 98.7% after 6000 cycles. This work provides a general strategy for the synthesis of edge-nitrogen-rich lignin-derived carbon materials for advanced potassium-ion storage.Graphical

Investigation on edge defect characteristics and electronic transport characteristics of graphene nano cutting

The effects of cutting crystal direction and speed on edge morphology, defects and electron transport characteristics were studied by molecular dynamics from the distribution state of defect atoms, the number of defect atoms, cutting force and radial distribution function. The edge defects of zigzag graphene nanoribbons were extracted, and the difficulty of forming different kinds of defects and the influence of different defects on band gap were studied by density functional theory. The results indicate that cutting graphene along the [010] (zigzag) direction has a smaller variance and smoother cutting. The obtained graphene nanoribbons have fewer defects and good edge quality. And the higher the cutting speed, the fewer defects of the graphene nanoribbons formed, resulting in smaller damage. The typical defects at the edges include 5–8–5 defect (double-vacancy defect), 5–9 SV defect (single-vacancy defect), stone wales (SW) defect, chain defect, crack defect and hole defect. The relationship between the magnitude of forming energy values produced by different defect types is as follows: crack defect > chain defect > SW defect > 5–9 SV defect > 5–8–5 defect > hole defect. Hole defect is the most difficult to form. The band gap width of the cut edge containing defects is smaller than that of the perfect graphene nanoribbon, resulting in the increase of the conductivity of the graphene nanoribbon in the direction of metal characteristics. The presence of defects can open the band gap with of intrinsic graphene.

Study of Modified Offset Trajectory for Bonnet Polishing Based on Lifting Bonnet Method.

The inability to converge at the edge of a workpiece during polishing affects the edge profile accuracy and surface quality of the workpiece. In this study, a bias trajectory generation method based on the lifting bonnet method that can maintain the morphology of polished edges is presented. Firstly, by establishing the polishing parameters and the decreasing rule in line with the principles of the lifting bonnet method, we obtained the residual height spacing, the radius of the polishing area, the centre offset position, and the pressing depth for each offset trajectory. Subsequently, the modified bias trajectory algorithm correction coefficients were obtained by fitting the edge trajectories using cubic Bessel curves, which were multiplied with the bias amount to obtain the final modified bias trajectory. Finally, an experiment was designed to compare the edge effect of the modified bias trajectory with the traditional grating trajectory. The experimental findings indicate that the reduction in edge collapse following the implementation of the modified offset trajectory was 1.30 μm. In contrast, the edge collapse after polishing with the traditional grating trajectory amounted to 98.67 μm. Moreover, the edge collapse ensuing traditional polishing trajectory was 75.9 times more pronounced than that observed after using the modified offset trajectory. It is shown that the modified bias trajectory method can not only maintain the original edge morphology of the workpiece but can also promote the convergence of the edge effect to a certain extent.

Using convolutional neural networks to support examiners in duct tape physical fit comparisons

This paper describes the construction and use of a machine-learning model to provide objective support for a physical fit examination of duct tapes. We present the ForensicFit package that can preprocess and database raw tape images. Using the processed tape image, we trained a convolutional neural network to compare tape edges and predict membership scores (i.e., fit or non-fit category). A dataset of nearly 2000 tapes and 4000 images was evaluated, including various quality grades: low, medium, and high, as well as two separation methods, scissor-cut and hand-torn. The model predicts medium-quality and high-quality scissor-cut tape more accurately than hand-torn, whereas for low-quality tape predicts the hand-torn tapes more accurately. These results are consistent with previous studies performed on the same datasets by analyst examinations. A method of pixel importance was also implemented to show which pixels are used to make the decision. This method can confirm some fit features that correspond with analyst-identified features, like edge morphology and backing pattern. This pilot study demonstrates the feasibility of computational algorithms to build physical fit databases and automated comparisons using deep neural networks, which can be used as a model for other materials.

MDFN: A Multi-level Dynamic Fusion Network with self-calibrated edge enhancement for lung nodule segmentation

Precise segmentation of lung nodules from the surrounding tissues provides radiologists with distinct boundary details, instrumental to the meticulous diagnosis and subsequent treatment of lung cancer. Although extensively applied in practice, the existing deep learning approaches are generally confronted with under-segmentation for intractable nodules with heterogeneous textures, blurred boundaries, adhesive structures and the loss of crucial spatial and edge details during the deep convolutional encoding. Aiming at these issues, we take full advantage of the complementarity between edge and region to enhance the restoration of complex edges and propose a 2D Multi-level Dynamic Fusion Network (MDFN) for precise lung nodule segmentation and fine-grained edge details. Firstly, we innovatively construct a multi-scale spatial and channel feature selection module (MSCFS) to enrich the receptive fields and capture highly correlated multi-scale contexts. Secondly, a self-calibrated encoder with MSCFS is adopted to establish the spatial dependency between long distance and channel to avert the loss of both global and local features. Thirdly, a novel deep feature fusion decoder is designed to separately generate the edge and region features of the same and different levels and dynamically integrate them to restore fine-grained nodule edge morphology. Various experiments on LUNA16 public dataset confirm that MDFN outperforms other advanced methods on the quantitative index appraisal and qualitative visualization analysis, with an average DSC of 89.19% and Hausdorff distance of 2.353 mm. By contrast, with the powerful blend of multi-level edge and region features, MDFN achieves more smooth and consistent boundary details, especially for nodules adhesive to surrounding issues.

Experimental research on milling of TC11 by scCO2-MQL and edge morphology

Titanium alloy is widely used in typical aerospace structural parts because of its excellent mechanical properties and corrosion resistance. However, the serious problem of tool wear in the machining process of titanium alloy often leads to the decline of machining quality. In this article, scCO2 MQL cutting experiment and dry cutting experiment of TC11 are carried out. The machined surface morphology, plastic deformation, residual stress and tool wear were analyzed. The results show that scCO2 MQL can effectively reduce the surface defects on the machined surface. The increase of cutting edge structure and blunt radius of the composite increases the plastic deformation degree of the material, as well as the residual stress and work hardening degree. The scCO2 MQL can effectively improve the cutting contact area and reduce tool joint wear.

Multi-sensor imaging of winter buried lakes in the Greenland Ice Sheet

Recent studies have highlighted that meltwater in supraglacial lakes (SLs) can be buried during frozen season in the Greenland Ice Sheet (GrIS). Meltwater in buried lakes (BLs) can even persist through the winter, disturbing the englacial thermal regime and providing an important buffer against GrIS's contribution to sea-level rise. However, little is known about the inter-annual BL dynamics in the GrIS, and there is no quantitative statistic about the overall buried percentage. Here, we conduct a satellite-based study to automatically map the winter BLs over the GrIS during 2017–2022 using multi-source optical and synthetic aperture radar (SAR) images on the Google Earth Engine (GEE) platform. To eliminate the interferences from other weak microwave reflecting surfaces, summer SLs are first extracted from Landsat 8 and Sentinel-2 images to determine the potential BL searching areas on winter Sentinel-1 images. A self-adaptive thresholding algorithm is proposed to extract BLs within the dilated summer SLs using histogram-based morphological edge detectors. BLs extracted by the proposed method and visual interpretation show a substantial agreement with a precision of 0.82 and a Kappa coefficient of 0.70. On average, a total buried lake area of 182.27 km2 was observed each winter during the period 2017–2022. BLs were mainly distributed in the Center-West, South-West and North-East Basins, with the majority occurring at elevations between 800 and 1700 m. In 2019–2020, a sudden extension of BLs was observed over the GrIS, especially in the North-East Basin where abnormally high temperatures and surface runoff were recorded. In 2021–2022, a widespread distribution of BLs in the South-West Basin was observed after abnormal snowfall. Overall, about 13% of the GrIS summer SLs can persist through winter, suggesting the potential for meltwater hydrofracture in winter over large areas.

Numerical modelling of shear cutting using particle methods

The use of Advanced High Strength Steel (AHSS) allows for lightweighting of sheet steel components, with maintained structural integrity of the part. However, AHSS grades show limitations in edge crack resistance, primarily influenced by sheared edge damage introduced by the shear cutting process. Numerical modelling of the shear cutting process can aid the understanding of the sheared edge damage, thus avoiding unforeseen edge cracking in the subsequent cold forming. However, the extreme deformations of the blank during the shear cutting process are likely to cause numerical instabilities and divergence using conventional Finite Element modelling. To overcome these challenges, this work presents the use of a particle-based numerical modelling method called the Particle Finite Element Method (PFEM). PFEM accurately solves some of the challenges encountered in shear cutting with the standard Finite Element method, such as large deformation, angular distortions, generation of new boundaries and presents an efficient way of transfer historical information from the old to the new mesh, minimising the results diffusion. The present work shows prediction of cut edge morphology of AHSS using a PFEM modelling scheme, where the numerical results are verified against experiments. With these results, the authors show new possibilities to obtain accurate numerical prediction of the shear cutting process, which promotes further advances in prediction of edge damaged related to shear cutting of AHSS.

P‐31: Research on Improvement of White High Reflectivity Ink Process

The UV curing rate of the ink film was improved by adjusting the exposure wavelength and energy parameters. At the same time, the edge morphology of white ink window was improved from positive trapezoid to inverted trapezoid.

Volume Kerja dan Waktu Penggilingan Tongkol Jagung pada Ukuran Produk/ Morfologi dalam Proses Ball Mill

The use of corncob powder is very wide in various industrial fields, namely as a source of dietary fiber, the basic material for making bio-plastics, partial replacement of cement in the manufacture of concrete, functional chemicals, oyster mushroom cultivation media. The most efficient method in producing corncob powder is the Ball Mill Process. The purpose of this study was to evaluate the effect of working volume and milling time on the characteristics of corn cob powder. The research procedures include specimen preparation (including cutting and drying processes) and milling processes. The results obtained were then characterized using a scanning laser microscope (LSM) to analyze the morphology and size of the product, lower working volume results in a smaller powder production with a needle-like sharp edge morphology and a higher work volume also results in a powder that has a blunt edge morphology. Although working volume can potentially be used to control particle size, this parameter has a direct effect on powder yield. As for the long milling time, it produces a smaller powder size than the faster milling time.Â

Combining the YOLOv4 Deep Learning Model with UAV Imagery Processing Technology in the Extraction and Quantization of Cracks in Bridges.

Bridges are often at risk due to the effects of natural disasters, such as earthquakes and typhoons. Bridge inspection assessments normally focus on cracks. However, numerous concrete structures with cracked surfaces are highly elevated or over water, and is not easily accessible to a bridge inspector. Furthermore, poor lighting under bridges and a complex visual background can hinder inspectors in their identification and measurement of cracks. In this study, cracks on bridge surfaces were photographed using a UAV-mounted camera. A YOLOv4 deep learning model was used to train a model for identifying cracks; the model was then employed in object detection. To perform the quantitative crack test, the images with identified cracks were first converted to grayscale images and then to binary images the using local thresholding method. Next, the two edge detection methods, Canny and morphological edge detectors were applied to the binary images to extract the edges of the cracks and obtain two types of crack edge images. Then, two scale methods, the planar marker method, and the total station measurement method, were used to calculate the actual size of the crack edge image. The results indicated that the model had an accuracy of 92%, with width measurements as precise as 0.22 mm. The proposed approach can thus enable bridge inspections and obtain objective and quantitative data.