High-resolution Digital Images Research Articles

Study on the strain partitioning behavior in heterostructured complex-concentrated alloy via high-resolution digital image correlation

Here, we investigate the tensile deformation behavior in a Ni42Fe30Cr12Mn8Al5Ti3 complex-concentrated alloy with heterogeneously-grained structure consisting of fine and ultrafine grains containing nano-sized L12 precipitates. It is found that Lüders band initiation and propagation account for the early stage of deformation. High-resolution digital image correlation investigation reveals that there is strong strain partitioning between fine and ultrafine-grained domains upon plastic deformation. Significant strain gradient across the domain-interfaces is thus established. Strain partitioning promoted dense microshear bands are observed in the deformed alloy. They are uniformly distributed and interacted with each other, resulting in the formation of strain concentration zones, which are mostly evolved in fine-grained domains and some of them extend into ultrafine-grained domains at high strain levels. Thus, the global plastic deformation of the alloy is synergistically accommodated. Finally, local high stress in strain concentration zones in ultrafine-grained domains promotes the formation of voids at triple junctions of grain boundaries.

High Performance Mg Alloy with Designed Microstructure and Phases.

A high strength and ductile Mg-Gd-Y-Zn-Zr alloy was designed and fabricated. The local strain evolution of the alloys during plastic deformation was analyzed using high-resolution digital image correlation (DIC). The results showed that the β particles, nano-sized γ' phases, and LPSO phases were distributed in the as-extruded alloy and a bimodal microstructure was exhibited, including elongated un-dynamic recrystallized grains and fine dynamic recrystallized grains. With increasing extrusion ratio, the grain size remained, with the volume fraction of dynamic recrystallization of the as-extruded alloy increasing from 30% to 75%, and the as-extruded alloy exhibited a high strength-ductility synergy, which is attributed to the grain refinement, extensive β particles, and elongated block-shaped LPSO phases. The strain evolution analysis showed that a strain-transfer from un-DRXed regions to adjacent DRXed regions and LPSO phases can promote uniform plastic deformation, which tends to improve the ductility of the alloy.

A handheld device for intra-cavity and ex vivo fluorescence imaging of breast conserving surgery margins with 5-aminolevulinic acid

BackgroundVisualization of cancer during breast conserving surgery (BCS) remains challenging; the BCS reoperation rate is reported to be 20-70% of patients. An urgent clinical need exists for real-time intraoperative visualization of breast carcinomas during BCS. We previously demonstrated the ability of a prototype imaging device to identify breast carcinoma in excised surgical specimens following 5-aminolevulinic acid (5-ALA) administration. However, this prototype device was not designed to image the surgical cavity for remaining carcinoma after the excised lumpectomy specimen is removed. A new handheld fluorescence (FL) imaging prototype device, designed to image both excised specimens and within the surgical cavity, was assessed in a clinical trial to evaluate its clinical utility for first-in-human, real-time intraoperative imaging during index BCS.ResultsThe imaging device combines consumer-grade imaging sensory technology with miniature light-emitting diodes (LEDs) and multiband optical filtering to capture high-resolution white light (WL) and FL digital images and videos. The technology allows for visualization of protoporphyrin IX (PpIX), which fluoresces red when excited by violet-blue light. To date, n=17\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$n = 17$$\\end{document} patients have received 20mgkg\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$20\\frac{\ ext {mg}}{\ extrm{kg}}$$\\end{document} bodyweight (BW) 5-ALA orally 2-4 h before imaging to facilitate the accumulation of PpIX within tumour cells. Tissue types were identified based on their colour appearance. Breast tumours in sectioned lumpectomies appeared red, which contrasted against the green connective tissues and orange-brown adipose tissues. In addition, ductal carcinoma in situ (DCIS) that was missed during intraoperative standard of care was identified at the surgical margin at <1 mm depth. In addition, artifacts due to the surgical drape, illumination, and blood within the surgical cavity were discovered.ConclusionsThis study has demonstrated the detection of a grossly occult positive margin intraoperatively. Artifacts from imaging within the surgical cavity have been identified, and potential mitigations have been proposed.Trial RegistrationClinicalTrials.gov Identifier: NCT01837225 (Trial start date is September 2010. It was registered to ClinicalTrials.gov retrospectively on April 23, 2013, then later updated on April 9, 2020, to reflect the introduction of the new imaging device.)

Super-resolution of digital rock images with hybrid attention multi-branch neural network

High-resolution digital rock images are vital in studying microstructure and fluid flow characterization of oil and gas reservoirs. Computed tomography (CT) is currently the primary tool to acquire digital rock images because of its non-destructiveness and three-dimensional imaging capability. However, the restriction of imaging capability of CT sets a compromise between image resolution and field of view (FOV). The Hybrid Attention Multi-branch Super-Resolution Network (HAMSR) proposed in this study aims to remedy this deficiency. HAMSR employs a limited discrete distribution to capture all possible high-order information and generate reliable high-resolution images. The network is built to prioritize the recovery of pores, cracks, and textures in digital rock images by introducing channel and spatial attention mechanisms. The structure together with modules of HAMSR have been designed and optimized to yield superior performance with fewer parameters and less training time. HAMSR and several contemporary advanced models are trained on the DeepRock-SR 2D dataset which contains lots of sandstone and carbonate images. It is evident that HAMSR-generated images have minimal noise, clearer edges, and more micropores. Quantitative calculations show that compared to traditional bicubic interpolation, HAMSR acquires a 2.669 dB and 1.376 dB increase in peak signal-to-noise ratio (PSNR) respectively on the test sets (27%–38% reduction in relative error). Furthermore, the high-resolution (HR) images reconstructed by HAMSR are generally consistent with real HR images concerning statistical characteristics such as porosity, pore size distribution, shape factor distribution, and two-point correlation function. This work provides a new super-resolution model to generate high-quality digital rock images for further analysis.

Abstract PO2-29-02: Towards Precision Medicine in Breast Imaging: A Novel Open Mammography Database with Tailor-Made 3D Image Retrieval for Artificial Intelligence and Teaching

Abstract Objectives: According to the World Health Organization (WHO), breast cancer is the most frequent malignant neoplasia and leading cause of cancer death among women worldwide. Low- and middle-income countries hold the worst survival rates mainly owing to a lack of access to appropriate diagnosis and treatment related resources. For proper early diagnosis, it is established that besides the physical structure itself (e.g., mammography units), there's a need for adequate interpretation of imaging and that might be a particularly major problem in low-income societies once there is a tendency of greater education setbacks. Mammography datasets can improve this resource-driven gap by enabling the development of artificial intelligence technologies (AI) which can make breast cancer diagnosis more accurate in a cost-effective and scalable way. We aim to create a new database of high quality digital mammography images suitable for AI development and education. Methods: Our mammography database was developed by means of retrospective selection of 100 exams performed by Hospital São Paulo - Federal University of São Paulo ranging from 2019 to 2023. The project is assumed to be safe, versatile, and usable, and required an extensive search for the appropriate tool. Ambra Health, an American company, has developed cloud-based software for medical image management and stood out as a viable alternative. Their platform meets international data security criteria, they also made the intended careful customization possible, in addition to the possibility of associating image and text attachments. The categories were created in accordance with the BI-RADS® descriptors, a wide range of clinical scenarios and additional materials available, and they served as the basis for the advanced search feature, which intuitively filters exams that meet the selected criteria simultaneously. The platform was integrated with an automatic anonymization system upon upload, ensuring data privacy. After submission, the exams are retained in a restricted area for anonymization verification, categorization, and attachment management, before being released to the end-user. So as to broaden geographic coverage, the descriptors were entered in American English, respecting the origin of the BI-RADS® lexicon, as for the website structure, automatic translation to the accessing browser standard language was selected. Results: Our website is active and available at http://mamografia.unifesp.br, with access granted upon a simple registration process. 941 mammography images from 100 anonymized cases, 62% of which include 3D images, can be filtered based on the combination of 113 clinical and imaging variables, as well as attachment availability. The language is adaptable to the user's native language, and categorized searches can be accessed directly from the browser or downloaded as customized datasets. Additionally, features such as saved searches or starred exams are also available. Conclusion: We have developed an online and free mammography database that is completely innovative by integrating various resources into a single platform. We provide high-resolution and 3D digital images that can be searched using an advanced search system. Moreover, we offer supplementary clinical information in various attachment formats, favoring a rich clinical correlation. In this way, we have achieved the ambivalence of our goal, which was to promote education and research. *"images speak louder than words" Database: https://mamografia.unifesp.br Tutorials: https://www.youtube.com/@Mamografiaunifesp e-mail: acesso@mamografia.unifesp.br password: acesso@mamografia12 (valid until dec/23) Citation Format: Natália Cordeiro, Gil Facina, Afonso Nazário, Vanessa Sanvido, Joaquim Araujo Neto, Morgana Silva, Simone Elias. Towards Precision Medicine in Breast Imaging: A Novel Open Mammography Database with Tailor-Made 3D Image Retrieval for Artificial Intelligence and Teaching [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-29-02.

BBMS: Investigation to Single Colour Image Visibility Improvement in Turbid Media through Biorthogonal Wavelet Based Depth-map Estimation

Electronic imaging needs good quality, high resolution (HR) digital images for highlighting finer details of the image. Normally images captured by ordinary digital camera are post pro cessed with available software instead of high cost CMOS sensor phased camera. Problem commonly faced with these images may get degraded due to the scattering media which deteriorates contrast, shifts colour, and make overall image whitish. Information from the distant objects suffer from poor medium transmission as well as noise amplification. Biorthogonal wavelet denoising (BWD), compress sparsely in frequency -time space, removes noise from depth estimation and makes the transmission smooth. Moreover, single image recovery is a serious challenge and ill posed problem. The recovered image improves compare to degraded image significantly and dis cards possibility of false edge detection as well as prevent colour shift and low contrast. Good quality images are found with objective evaluation. Moreover, different wavelets, thresholds, and decomposition levels have been studied and compared. Time complexity is linear due to wavelet domain analysis which makes the technique fast, reliable, and visually pleasing.

Micro-mechanics investigation of heterogeneous deformation fields and crack initiation driven by the local stored energy density in austenitic stainless steel welded joints

This work investigates the heterogeneous deformation and failure of HR3C austenitic stainless steel welded joints at room and typical service temperatures. Such types of welded joints are widely used in the new generation of fossil fuel power stations and are known to suffer from premature high temperature failure. Observation of in-service failures revealed that cracks may nucleate either in the heat affected zone or in the weld metal. The main objective of this work is to identify the local microstructural conditions and stress–strain fields responsible for both ductile failure and micro-crack nucleation within the weldment at low and high temperatures, respectively. To that purpose, a novel multi-scale micromechanics-based modelling framework is proposed. It relies on representative weld microstructure models digitally reconstructed from electron backscatter diffraction measurements, and dislocation density-based crystal plasticity models to describe the behaviour of individual grains in each weld region. A novel thermo-dynamically consistent relation for the configuration stored energy per unit volume is derived from the crystal plasticity framework.The single crystal models are calibrated and validated from a combination of uniaxial tensile tests on cross-weld specimens using high resolution digital image correlation techniques, representative volume elements of the polycrystals at the macro-scale, as well as micropillar compression tests at the scale of the individual grains. Predictions of heterogeneous inelastic deformation fields within the weldment at both 22 °C and 665 °C, and of micropillar compression behaviour of the individual weld single crystal phases are consistent with experimental data. The experimentally observed ductile failure of the weld metal at room temperature is successfully predicted using a Gurson-type void nucleation, growth and coalescence constitutive model. The suitability of the stored strain energy density and the accumulated plastic strain as crack initiation/creep failure indicators is investigated. It was found that predicted creep lifetimes with either indicator were very similar, and that they were relatively accurate for low stress levels.

Winter Wheat Yield Estimation with Color Index Fusion Texture Feature

The rapid and accurate estimation of crop yield is of great importance for large-scale agricultural production and national food security. Using winter wheat as the research object, the effects of color indexes, texture feature and fusion index on yield estimation were investigated based on unmanned aerial vehicle (UAV) high-definition digital images, which can provide a reliable technical means for the high-precision yield estimation of winter wheat. In total, 22 visible color indexes were extracted using UAV high-resolution digital images, and a total of 24 texture features in red, green, and blue bands extracted by ENVI 5.3 were correlated with yield, while color indexes and texture features with high correlation and fusion indexes were selected to establish yield estimation models for flagging, flowering and filling stages using partial least squares regression (PLSR) and random forest (RF). The yield estimation model constructed with color indexes at the flagging and flowering stages, along with texture characteristics and fusion indexes at the filling stage, had the best accuracy, with R2 values of 0.70, 0.71 and 0.76 and RMSE values of 808.95 kg/hm2, 794.77 kg/hm2 and 728.85 kg/hm2, respectively. The accuracy of winter wheat yield estimation using PLSR at the flagging, flowering, and filling stages was better than that of RF winter wheat estimation, and the accuracy of winter wheat yield estimation using the fusion feature index was better than that of color and texture feature indexes; the distribution maps of yield results are in good agreement with those of the actual test fields. Thus, this study can provide a scientific reference for estimating winter wheat yield based on UAV digital images and provide a reference for agricultural farm management.

Super-resolution reconstruction of 3D digital rocks by deep neural networks

Digital rock technology provides valuable insights into the pore structure and fluid flow properties of geoenergy resources. Artificial intelligence technology in vision and image processing, especially the image super-resolution, has great potential for digital rock reconstruction and resolution enhancement. However, the analyzed core samples are typically sandstones/carbonates in micro-scale resolutions and in two-dimensional (2D) space, whereas the shale rocks in nano-scale resolutions for unconventional resources or three-dimensional (3D) digital cores are rarely investigated. Additionally, previous studies primarily emphasized image quality from a computer vision perspective, with little consideration given to estimating physical properties of digital rocks using super-resolution techniques.This study presents a very deep super-resolution (VDSR) algorithm, specifically designed to generate high-resolution 3D digital rock images, for nano-scale shale matrix and micro-scale hydraulic fractures. We compare both image quality and permeability accuracy between the original high-resolution images and the super-resolution images reconstructed by the proposed method. The results reveal that the reconstructed images using the proposed method closely resemble the actual images, and effectively reduce errors in permeability computations. This study highlights the applicability of the proposed VDSR algorithm in establishing the detailed structures of 3D nano-scale shale matrix and hydraulic fractured rocks, thus advancing super-resolution techniques in digital core analysis for geoenergy resources development.

Hydrodynamic Study of a Hybrid Electro-Flotation Column

Bubble columns are used in the mining industry for mineral recovery but are also widely utilized in the chemical and petrochemical industry. The hydrodynamic characteristics of their performance is a field of interest with a number of points, which are nonetheless poorly understood, and a considerable amount of methods have aimed to shed light on the flow regimes that prevail in the columns. The study of the hydrodynamic part of a flotation process should consider characteristics such as air flow, volumetric gas fraction, flow field, and bubble size, along with the mechanical and design factors and pulp properties. The present work aims to elucidate the characteristics of the gas phase of a hybrid flotation system. For this purpose, a hybrid flotation column was designed and constructed and the bubbles size distributions at different radial positions in the flotation column were computed by analyzing high resolution digital images. A patented electrical impedance technique was employed to instantaneously measure the local volumetric gas fraction. Flow dispersion in the column was studied by residence time distributions using conductivity tracers. The experimental results are discussed to comprehend the variation in the gas fraction in the column. In particular, the study showed that the size of the bubbles changed from the center to the walls of the column, and this was observed both radically and vertically. Moreover, the size of the bubbles affected the volume fractions, and no coalescence of the bubbles was observed. Finally, the dispersion of the tracer in the working solution was distributed uniformly in the volume of the column, with a time difference for the four positions of the column.

Strain partitioning in dual-phase eutectic high-entropy alloy: Dependence on phase boundary morphology

Strain partitioning in dual-phase eutectic high-entropy alloy, AlCoCrFeNi2.1, is investigated under uniaxial tension with in situ high-resolution digital image correlation (DIC) in a scanning electron microscope (SEM). Nanoscale speckles are fabricated on the sample surface. Three regions with different phase morphologies at different applied strains are characterized by SEM for DIC analysis. Strain localization tends to form at softer face-centered cubic (FCC) rather than body-centered cubic phase domains, and the degree of strain localization in a lamellar structure is much lower than in a bimodal structure. Transmission electron microscopy is conducted to characterize the phase interaction at the nanoscale. A straight phase boundary in a lamellar structure allows more geometrically necessary dislocations to pile up at the phase boundary and form a wide interface affected zone (IAZ) in the soft FCC domain. The IAZ strengthens the soft FCC and better accommodates the deformation of the two phases, resulting in a relatively uniform strain partitioning.

A detachable interface for stable low-voltage stretchable transistor arrays and high-resolution X-ray imaging

Challenges associated with stretchable optoelectronic devices, such as pixel size, power consumption and stability, severely brock their realization in high-resolution digital imaging. Herein, we develop a universal detachable interface technique that allows uniform, damage-free and reproducible integration of micropatterned stretchable electrodes for pixel-dense intrinsically stretchable organic transistor arrays. Benefiting from the ideal heterocontact and short channel length (2 μm) in our transistors, switching current ratio exceeding 106, device density of 41,000 transistors/cm2, operational voltage down to 5 V and excellent stability are simultaneously achieved. The resultant stretchable transistor-based image sensors exhibit ultrasensitive X-ray detection and high-resolution imaging capability. A megapixel image is demonstrated, which is unprecedented for stretchable direct-conversion X-ray detectors. These results forge a bright future for the stretchable photonic integration toward next-generation visualization equipment.

ІНФОРМАЦІЙНА ТЕХНОЛОГІЯ ПОПЕРЕДНЬОЇ ОБРОБКИ СУПУТНИКОВИХ ЗОБРАЖЕНЬ З ВИКОРИСТАННЯМ ЗГОРТКОВОЇ НЕЙРОННОЇ МЕРЕЖІ

With urbanization and the growing interest in geographic information systems, preci-sion mapping, and environmental monitoring, there is a need to improve methods of fusing high-resolution digital satellite images to obtain more detailed and informative data. Satellite images with high spatial resolution hold significant promise for addressing a variety of is-sues. However their utility is frequently constrained by limited contrast dynamics and re-stricted spectral data. Pansharpening methods are effective means of increasing the spatial resolution of multichannel images using panchromatic ones. Although there are a significant number of proposed pansharpening algorithms, none can be universal for all types of sensors and demonstrate different results. The paper reviews the fusion methods of high spatial reso-lution satellite images and proposes an information technology for preprocessing digital sat-ellite images using a convolutional neural network. It made it possible to effectively extract texture details and spatial dependencies for local and non-local information. The paper com-pares the results of the proposed technology with existing pan-sharpening methods, such as Gram-Shmidt, HSV, Wav+HSV, ATWT, AWLP, BDSD, Brovey, and HPF. The results of test-ing conducted on eight-channel images acquired by the WorldView-2 satellite confirm that the proposed technology (ERGAS=0.32, RMSE=0.28b SSIM=0.71, PSNR=37.60) allows for achieving high spectral and spatial quality of multichannel images and outperforms existing methods.

Analysis of slip transfer across grain boundaries in Ti via diffraction contrast tomography and high-resolution digital image correlation: When the geometrical criteria are not sufficient

High-resolution digital image correlation was employed to analyze the occurrence of slip transfer across grain boundaries in pure Ti. The microstructure was characterized in 3D through diffraction contrast tomography and electron backscatter diffraction. The prismatic slip activity was captured in the shear strain maps at different strains, and the grain boundaries were classified according to the occurrence/absence of slip transfer. Different geometrical slip transfer criteria were tested, and, in general, the slip transfer likelihood increased when the slip systems across the boundary were well-aligned. Nevertheless, a detailed analysis of selected grain boundaries revealed that slip transfer could be triggered or impeded depending on the local stresses acting on the neighborhood, regardless of the alignment between the slip systems. The microstructural and deformation patterns where the geometrical criteria are not enough to predict slip transfer are assessed.

Strain localization in the Alloy 718 Ni-based superalloy: From room temperature to 650 °C

Irreversible deformation in relation to the microstructure was investigated for a polycrystalline Ni-based superalloy (Alloy 718) from room temperature to 650 °C using high-resolution digital image correlation (HR-DIC) techniques. Interrupted tensile tests were performed under a protective atmosphere to ensure the stability of the speckle pattern to track kinematics fields from surface analyses. In-plane strain localization was captured using HR-DIC on scanning electron microscopy (SEM) images. A statistical analysis of different strain localization events in relation to the microstructural features was conducted, i.e., intragranular slip localization, slip localization parallel to and near Σ3-twin boundaries (Σ3-TB), and grain boundary sliding (GBS). Alloy 718 exhibited slip localization at room temperature and 350 °C. Intense strain localization develops parallel and in the vicinity of Σ3-TB from the onset of the microplasticity. Few intense slip stimulated-grain boundary sliding events were found due to slip localization on both grains adjacent to the grain boundary. At 650 °C, Alloy 718 experienced grain boundary sliding at the onset of the yield without particular slip localization in adjacent grains. At lower temperatures, strain localization parallel to and near Σ3-TB was intense, and intragranular slip localization intensified with increasing macroscopic deformation. Particular microstructural configurations were found at 650 °C leading to premature damage: (i) sub-surface cavitation at grain boundaries, and (ii) grain boundary cracking due to intense shearing near a Σ3-TB.

(005) Immunohistochemical Staining of the 12:00 Vestibule in NPV Patients: First Ever Assessment

Abstract Introduction In 2004, Bornstein and colleagues performed immunohistochemical (IHC) staining of excised vestibular specimens in patients with vestibular pain. They demonstrated that these specimens contained &amp;gt; 8 CD117 immunopositive stained cells per high-power field (HPF), consistent with mast cells, and excess S100 stained immunopositive cells, consistent with nerves-far more compared to control tissue. However, Bornstein et al. only studied the 1:00-11:00 vestibule in this condition, now referred to as neuroproliferative vestibulodynia (NPV). To the best of our knowledge, the 12:00 region of the vestibule, below the clitoral frenulae and above the urethral meatus, has not been examined with IHC staining. Pain in the 12:00 region can be particularly bothersome for patients and poses several challenges for successful treatment. Objective To report IHC staining findings in the excised 12:00 vestibule specimen and associated clinical characteristics of NPV patients. Methods Retrospective chart review performed. Slides of IHC-stained vestibular tissue specimens from the 12:00 vestibule were examined and high-resolution digital microscopy images were captured under 100x and 200x magnification. A least 2 photomicrographs at both magnifications were taken for each slide that included representative areas of epithelial basement membrane and the adjacent subepithelial regions. Fractional area of positive immunostaining in all photomicrographs was assessed and mean values calculated using computer-assisted histometry by ImageJ. Results A total of 50 patients had complete vestibulectomy including 12:00 and 3 had regional vestibulectomy for only the 12:00 area. Using manual counting as applied by Bornstein et al., 53 excised 12:00 specimens stained for CD117 had a median immunopositive cell count, consistent with mast cells, of 29.5, greater than the described 8 mast cells per HPF. Using computer-assisted histometry, tissue stained for CD117 had a median fractional area of 0.73 in the 12:00 region. Tissues stained for PGP9.5 had a median fractional area of 0.31 in the 12:00 region. Overall, there was no difference between lifelong NPV (LNPV) and acquired NPV (ANPV) patients in the cell count or median fractional area for CD117-immunopositive or PGP9.5-immunopositive staining in the 12:00 region. In LNPV patients who had excision of both the 1:00-11:00 and 12:00 regions, the 12:00 region had significantly less PGP9.5 immunopositive fractional area than the 1:00-11:00 region of the vestibule (p = 0.001). Their H&amp;E pathology findings, including severity grading of the subepithelial inflammatory infiltrate, did not predict the IHC assessment of either CD117 or PGP9.5 in the 12:00 region. The median cotton-tipped swab test pain score was 5 out of 10 for the 12:00 region vs 7 for the rest of the vestibule. Conclusions Current surgical practices often do not include the 12:00 region, likely due to lack of data demonstrating excess mast cells and nerves in this specific region. Our results suggest that there are, in fact, a high density of immunopositive histochemical stains consistent with mast cells and nerves in the 12:00 region and high reported pain scores in both 12:00 and 1:00-11:00 vestibule. Thus, NPV surgical treatments should include, when appropriate, the 12:00 vestibule, as NPV is a field disease involving the entire vestibule. Disclosure No.

A Novel Technique Based on Machine Learning for Detecting and Segmenting Trees in Very High Resolution Digital Images from Unmanned Aerial Vehicles

The present study proposes a technique for automated tree crown detection and segmentation in digital images derived from unmanned aerial vehicles (UAVs) using a machine learning (ML) algorithm named Detectron2. The technique, which was developed in the python programming language, receives as input images with object boundary information. After training on sets of data, it is able to set its own object boundaries. In the present study, the algorithm was trained for tree crown detection and segmentation. The test bed consisted of UAV imagery of an agricultural field of tangerine trees in the city of Palermo in Sicily, Italy. The algorithm’s output was the accurate boundary of each tree. The output from the developed algorithm was compared against the results of tree boundary segmentation generated by the Support Vector Machine (SVM) supervised classifier, which has proven to be a very promising object segmentation method. The results from the two methods were compared with the most accurate yet time-consuming method, direct digitalization. For accuracy assessment purposes, the detected area efficiency, skipped area rate, and false area rate were estimated for both methods. The results showed that the Detectron2 algorithm is more efficient in segmenting the relevant data when compared to the SVM model in two out of the three indices. Specifically, the Detectron2 algorithm exhibited a 0.959% and 0.041% fidelity rate on the common detected and skipped area rate, respectively, when compared with the digitalization method. The SVM exhibited 0.902% and 0.097%, respectively. On the other hand, the SVM classification generated better false detected area results, with 0.035% accuracy, compared to the Detectron2 algorithm’s 0.056%. Having an accurate estimation of the tree boundaries from the Detectron2 algorithm, the tree health assessment was evaluated last. For this to happen, three different vegetation indices were produced (NDVI, GLI and VARI). All those indices showed tree health as average. All in all, the results demonstrated the ability of the technique to detect and segment trees from UAV imagery.

Quantifying cracking and strain localisation in a cold spray chromium coating on a zirconium alloy substrate under tensile loading at room temperature

The mechanical and cracking behaviour of a Cr coating, deposited on an Optimized ZIRLOTM substrate using the Cold Spraying (CS) technique, was investigated by in-situ scanning electron microscopy (SEM) tensile testing at room temperature. To achieve this, curved micro-tensile samples were machined from cladding tubes, and a styrene-assisted gold remodelled pattern was applied on the coating surface. The macroscopic crack density of the coating with applied axial strain was quantified through automated SEM image processing, whilst the localisation of strain was monitored with High Resolution Digital Image Correlation (HRDIC). Like with most brittle thin films deposited on ductile substrates, under applied external stress, parallel crack channelling appeared on the coating surface, perpendicular to the loading direction. The first crack was captured at 0.5% axial strain, whilst saturation was observed at 1.5%. Single crack formation in the coating was found to cause relaxation of local strains and redistribution towards the uncracked segment's midpoint. The local fracture strain of the coating was found to increase with the decreasing crack spacing, whilst shear bands appeared on the coating's surface at 35° and 145° with respect to the loading direction, which is evidence of plastic deformation. The crack density of the curved sample geometry was correlated with the tubes using a modified shear-lag model, considering the residual stresses in the coating measured by X-ray Diffraction (XRD). The CS Cr coating showed high levels of bonding and mechanical interlocking with the substrate, even after 3% axial strain.

Micro-strain and cyclic slip accumulation in a polycrystalline nickel-based superalloy

This work provides a comprehensive characterization and analysis of deformation and fatigue damage mechanisms in a nickel-based superalloy during ambient temperature fatigue and points to a fundamental deformation mechanism that results in the onset of crack nucleation. Strain and slip irreversibility are investigated at the nanometer scale using high-resolution digital image correlation and high-resolution electron backscatter diffraction, highlighting distinct deformation mechanisms contributing to crack nucleation. It is observed during early fatigue cycling at relatively low applied stress, the formation of intense slip events that induce grain boundary shearing. This results in intense micro-scale strain in the neighboring grains, producing localized plasticity and stresses. Such stresses facilitate fatigue extrusion–intrusion mechanisms during subsequent cycling, resulting in preferred crack nucleation. Finally, the configurations within the microstructure that promote such deformation and damage mechanisms sequence are highlighted.

Applications of Digital Pathology in Cancer: A Comprehensive Review

Digital pathology, powered by whole-slide imaging technology, has the potential to transform the landscape of cancer research and diagnosis. By converting traditional histopathological specimens into high-resolution digital images, it paves the way for computer-aided analysis, uncovering a new horizon for the integration of artificial intelligence (AI) and machine learning (ML). The accuracy of AI- and ML-driven tools in distinguishing benign from malignant tumors and predicting patient outcomes has ushered in an era of unprecedented opportunities in cancer care. However, this promising field also presents substantial challenges, such as data security, ethical considerations, and the need for standardization. In this review, we delve into the needs that digital pathology addresses in cancer research, the opportunities it presents, its inherent potential, and the challenges it faces. The goal of this review is to stimulate a comprehensive discourse on harnessing digital pathology and AI in health care, with an emphasis on cancer diagnosis and research.