Dimensional Shape Research Articles

Modeling and simulation of welded joints of SS304H & P91 Steels: A Review

Most of the structures are fabricated by welded joints in various manufacturing units because of low cost and high strength. Welding process is largely used in almost all manufacturing units. Welded joints usually subject to welding deformation patterns. Welding deformation may lead to low dimensional accuracy, shape and aesthetics of the product, strength of the welded joint. Welding of two different materials having different mechanical properties is called dissimilar welding. Dissimilar welded joints are commonly used in power plants to connect martensitic steel components and austenitic stainless steel piping systems. Our approach involves conducting dissimilar welding on P91 and SS304H steels, and subsequently assessing the properties of the welded joints using simulation software. A 3-D thermal elastic plastic finite element computational process is designed to accurately forecast welding deformation by numerical method. Numerical and experimental outcomes were compared in terms of temperature distributions during welding and in terms of distortion. P91 is a chromium-molybdenum alloy known for its remarkable strength and exceptional resistance to high temperatures. Alloy SS304H represents an adaptation of the chromium-nickel austenitic stainless steel. This variant, Grade 304H stainless steel, offers enhanced heat-resistant properties, increased tensile yield strength, and improved short- and long-term creep strength.

Geometric phases of nonlinear elastic [formula omitted]-rotors via Cartan’s moving frames

We study the geometric phases of nonlinear elastic N-rotors with continuous rotational symmetry. In the Hamiltonian framework, the geometric structure of the phase space is a principal fiber bundle, i.e., a base, or shape manifold B, and fibers F along the symmetry direction attached to it. The symplectic structure of the Hamiltonian dynamics determines the connection and curvature forms of the shape manifold. Using Cartan’s structural equations with zero torsion we find an intrinsic (pseudo) Riemannian metric for the shape manifold. Without lose of generality, we show that one has the freedom to define the rotation sign of the total angular momentum A of the elastic rotors as either positive or negative, e.g., counterclockwise or clockwise, respectively, or viceversa. This endows the base manifold B with two distinct metrics both compatible with the geometric phase. In particular, the metric is pseudo-Riemannian if A<0, and the shape manifold is a 2D Robertson-Walker spacetime with positive curvature. For A>0, the shape manifold is the hyperbolic plane H2 with negative curvature. We then generalize our results to free elastic N-rotors. We show that the associated shape manifold B is reducible to the product manifold of N−1 hyperbolic planes H2 (A>0), or 2D Robertson-Walker spacetimes (A<0) depending on the convection used to define the rotation sign of the total angular momentum. We then consider elastic N-rotors subject to time-dependent self-equilibrated moments. The N-dimensional shape manifold of the extended autonomous system has a structure similar to that of the (N−1)-dimensional shape manifold of free elastic rotors. The Riemannian structure of the shape manifold provides an intrinsic measure of the closeness of one shape to another in terms of curvature, or induced geometric phase.

Genotype-Phenotype Taxonomy of Hypertrophic Cardiomyopathy.

Hypertrophic cardiomyopathy (HCM) is an important cause of sudden cardiac death associated with heterogeneous phenotypes, but there is no systematic framework for classifying morphology or assessing associated risks. Here, we quantitatively survey genotype-phenotype associations in HCM to derive a data-driven taxonomy of disease expression. We enrolled 436 patients with HCM (median age, 60 years; 28.8% women) with clinical, genetic, and imaging data. An independent cohort of 60 patients with HCM from Singapore (median age, 59 years; 11% women) and a reference population from the UK Biobank (n=16 691; mean age, 55 years; 52.5% women) were also recruited. We used machine learning to analyze the 3-dimensional structure of the left ventricle from cardiac magnetic resonance imaging and build a tree-based classification of HCM phenotypes. Genotype and mortality risk distributions were projected on the tree. Carriers of pathogenic or likely pathogenic variants for HCM had lower left ventricular mass, but greater basal septal hypertrophy, with reduced life span (mean follow-up, 9.9 years) compared with genotype negative individuals (hazard ratio, 2.66 [95% CI, 1.42-4.96]; P<0.002). Four main phenotypic branches were identified using unsupervised learning of 3-dimensional shape: (1) nonsarcomeric hypertrophy with coexisting hypertension; (2) diffuse and basal asymmetrical hypertrophy associated with outflow tract obstruction; (3) isolated basal hypertrophy; and (4) milder nonobstructive hypertrophy enriched for familial sarcomeric HCM (odds ratio for pathogenic or likely pathogenic variants, 2.18 [95% CI, 1.93-2.28]; P=0.0001). Polygenic risk for HCM was also associated with different patterns and degrees of disease expression. The model was generalizable to an independent cohort (trustworthiness, M1: 0.86-0.88). We report a data-driven taxonomy of HCM for identifying groups of patients with similar morphology while preserving a continuum of disease severity, genetic risk, and outcomes. This approach will be of value in understanding the causes and consequences of disease diversity.

An Inspection Technique Using Fit Clearance Based on Microscopic Vision in Precision Assembly.

Inspection is a crucial process to ensure product quality. In the precision assembly of an optic-mechanical device, a part with micro multi-section arcs needs to be inspected and assembled into another part. Actually, because of machining errors, including dimensional and geometric shapes, can lead to complex deformation modes for parts with micro multi-section arcs, posing challenges to their inspection. Furthermore, inconsistencies in feature images in microscopic vision may complicate the extraction of the Region of Interest (ROI). To address these issues, this paper proposes an ROI extraction method based on the CAD model for rough positioning of feature points and connected region detection for refinement. Subsequently, based on feature points, the CAD model is used again to obtain the ROI. For inspection purposes, this paper proposes a method suitable for micro multi-section arcs based on assembly fit requirements. Experimental testing was performed on parts with eight-section arcs and mirrors to verify the effectiveness of the proposed method. This method provides a suitable solution for the inspection of micro multi-section arcs in precision assembly with the potential to improve the accuracy of the inspection results.

Evaluating the dynamic behavior of railway-bridge transition zone: numerical and field measurements

Short-span bridges are one of the most frequent infrastructures along railway tracks where railway track stiffness suddenly changes. The sudden variation in the vertical stiffness of railway tracks increases dynamic loads and causes numerous defects in ballasted railroads. Therefore, improving the dynamic performance of railway tracks can be conducted by constructing countermeasures along the transition zone. In this regard, the approach slab is a practical technique used in railway-bridge transition zones. As the dimensional shape of the approach slab plays a significant role in the dynamic response of transition zones, this study evaluated the effects of its main geometric parameters. A three-dimensional model of the railway portal bridge, including the approach slab, was built using the finite element method and analyzed by imposing moving wheel loads as a series of acting force points along rail elements. The model was validated with field-obtained results acquired through a laser/camera-based measuring technique. Then, some sensitivity analyses were performed to find optimized geometric dimensions of approach slabs to improve the dynamic behavior of railway-bridge transition zones. Obtained results of the geometrical sensitivity analysis show that when the geometrically optimized approach slab is used along the railway-bridge transition zone, the displacements of rail and ballast are decreased by 24% and 18%, respectively.

Sex differences in airway volume and 3-dimensional shape in Japanese adults

(1) To establish normative data for three-dimensional (3D) measurements of the upper airway in young Japanese adults, and (2) to investigate sex-related differences in linear and volumetric measurements, as well as shape. This study employed cone-beam computed tomography (CBCT) images of 56 Japanese young adults preselected from among 1000 patients, so that samples matched a historic 2D cephalometric cohort with normal occlusion using propensity score matching. Three-dimensional models of the oropharynx and hypopharynx were reconstructed from CBCT images and their volumes were calculated. We defined 20 landmarks on the surface of the 3D model and performed seven linear measurements between them. The mean and standard deviation of the linear measurements were calculated as the normative data for each sex as well as the volumes. Sex-related differences were analyzed using t-test (p < 0.05). Principal component discriminant analysis of the coordinate values of the landmarks was also performed to examine sex differences in shape. The normative ranges of the 3D measurements of the oropharynx and hypopharynx were determined according to sex. Sex-related differences in the measurement results were observed in hypopharyngeal length but not in volume. The hypopharynx length in males was significantly longer than that in females. The discriminant analysis showed that males tended to show longer and straight shapes, while females showed inversed triangular shapes from the frontal view. This result will allow clinicians to evaluate how patient airway characteristics differ from the normative 3D morphology of the upper airway.

Regional plantar forces and surface geometry variations of a chronic ankle instability population described by statistical shape modelling

BackgroundUnderstanding detailed foot morphology as well as regional plantar forces could provide insight into foot function and provide recommendation for footwear design for chronic ankle instability (CAI) people. Research QuestionThis study presented 3-dimensional statistical shape models of feet from three different populations including CAI, copers and healthy individuals, with regional plantar forces also acquired. MethodsSixty-six males (22 participants per group) were included in this study to capture 3-dimensional foot shapes under a standing condition and regional plantar forces during a cutting maneuver. Principal component analysis was performed to generate a mean foot shape of each group as well as modes of variations. A generalized procrustes analysis was used to achieve rapid registration of mean shapes. Besides, regional plantar forces and contact duration among these three populations were compared. ResultsFor 3-dimensional foot shapes, although no significant differences of the average distance between each mode and mean shape were found among three populations, there were subtle variations in mean shapes. The CAI population presented a more bulging of the lateral malleolus; copers were characterized by the flexion of the lesser toes, a more bulging of the medial foot in the sagittal plane; and healthy individuals showed a greater heel width and a more bulging of the heel in the sagittal plane. In terms of plantar forces, healthy individuals had significantly greater summated plantar forces and greater plantar forces in the lateral heel area during the early contact phase compared to copers and CAI participants. SignificanceOverall, this study suggested that repetitive ankle sprains may lead to the bulging of the lateral malleolus. Further, CAI and copers seem to stabilize the ankle joint by medially shifting the center of pressure compared to healthy individuals under the static and less challenging dynamic conditions.

3D Animal Illustration Flashcard as a Learning Media Innovation in Early Childhood Education

Augmented reality (AR) is a technology that allows users to interact with virtual elements in a real-world environment. In the context of early childhood education, AR-based learning media can provide a number of benefits for children learning about animals. With the assistance of parents, children can learn 3-dimensional shapes and animal characters that are integrated with smartpones. The aim of this research is to develope 3D flashcards based on Augmented Reality for learning media in Early Childhood Education. The developed flashcards are also given the distinctive sound of each animal so that the child can easily recognize it. The product was developed using ADDIE (Analyze, Design, Develompent, Implementation and Evaluation), and it was then tested on a limited group, and validated to get input for improvements before being mass-produced. The result of this research in AR-based learning media is that it can make learning about animals more engaging and interactive for children. This can make the learning process more fun and interesting for children, and can also help to retain their attention and focus during the learning process. Another benefit of using AR-based learning media is that it can help to make the information more relatable and concrete for children. For instance, instead of just reading about a cow in a flashcard, children can use an AR app to see a 3D model of a cow in front of them and even interact with it. The limitation of this study is that from the results of the trial, the data shows that the use of paper-based flasshcards can be damaging in early childhood, and teachers suggest using sturdier materials such as PVC.&#x0D; Keywords: flashcards, augmented reality, animals, early childhood, education How to

Three-dimensional shape generation via variational autoencoder generative adversarial network with signed distance function

&lt;span lang="EN-US"&gt;Mesh-based 3-dimensional (3D) shape generation from a 2-dimensional (2D) image using a convolution neural network (CNN) framework is an open problem in the computer graphics and vision domains. Most existing CNN-based frameworks lack robust algorithms that can scale well without combining different shape parts. Also, most CNN-based algorithms lack suitable 3D data representations that can fit into CNN without modification(s) to produce high-quality 3D shapes. This paper presents an approach that integrates a variational autoencoder (VAE) and a generative adversarial network (GAN) called 3 dimensional variational autoencoder signed distance function generative adversarial network (3D-VAE-SDFGAN) to create a 3D shape from a 2D image that considerably improves scalability and visual quality. The proposed method only feeds a single 2D image into the network to produce a mesh-based 3D shape. The network encodes a 2D image of the 3D object into the latent representations, and implicit surface representations of 3D objects corresponding to those 2D images are subsequently generated. Hence, a signed distance function (SDF) is proposed to maintain object inside-outside information in the implicit surface representation. Polygon mesh surfaces are then produced using the marching cubes algorithm. The ShapeNet dataset was used in the experiments to evaluate the proposed 3D-VAE-SDFGAN. The experimental results show that 3D-VAE-SDFGAN outperforms other state-of-the-art models.&lt;/span&gt;

Features of teaching supplements designed to help primary teachers reduce student misconceptions in mathematics

The findings of teacher misconceptions of mathematics at various levels indicate a wide variation of mathematics taught to students. This research aims to produce valid, practical, and effective teaching supplementary materials for integers, fractions, two dimensional and three-dimensional shapes that can overcome misconceptions. This two-year development research applied Plomp's development phases: preliminary investigation, design, realization, test phase, evaluation, and revision. The data were analyzed from teacher misconceptions about mathematics from the first year of the study, the development of teaching material supplements, the results of observations on the implementation of teaching materials, the teacher's response questionnaire to the implementation of the teaching materials, and test results. The research subjects were primary school teachers in Sidoarjo who experienced mathematical misconceptions. The results showed that the teaching material supplements developed met valid, practical, and effective criteria based on expert validation, teacher responses, and teacher work results on the assessment sheets. The features of the supplements were developed based on cognitive conflict and the resolution of conflicting perspectives and the teachers' existing ideas and extend them, through, for example, the analogy to a new domain, where those are presented in either the materials or the assessments.

Molecular system for an exponentially fast growing programmable synthetic polymer

In this paper, we demonstrate a molecular system for the first active self-assembly linear DNA polymer that exhibits programmable molecular exponential growth in real time, also the first to implement “internal” parallel insertion that does not rely on adding successive layers to “external” edges for growth. Approaches like this can produce enhanced exponential growth behavior that is less limited by volume and external surface interference, for an early step toward efficiently building two and three dimensional shapes in logarithmic time. We experimentally demonstrate the division of these polymers via the addition of a single DNA complex that competes with the insertion mechanism and results in the exponential growth of a population of polymers per unit time. In the supplementary material, we note that an “extension” beyond conventional Turing machine theory is needed to theoretically analyze exponential growth itself in programmable physical systems. Sequential physical Turing Machines that run a roughly constant number of Turing steps per unit time cannot achieve an exponential growth of structure per time. In contrast, the “active” self-assembly model in this paper, computationally equivalent to a Push-Down Automaton, is exponentially fast when implemented in molecules, but is taxonomically less powerful than a Turing machine. In this sense, a physical Push-Down Automaton can be more powerful than a sequential physical Turing Machine, even though the Turing Machine can compute any computable function. A need for an “extended” computational/physical theory arises, described in the supplementary material section S1.

Synthetic Peptide Antibodies as TNF-α Inhibitors: Molecularly Imprinted Polymer Nanogels Neutralize the Inflammatory Activity of TNF-α in THP-1 Derived Macrophages.

Tumor Necrosis Factor-α (TNF-α) is a cytokine that is normally produced by immune cells when fighting an infection. But, when too much TNF-α is produced as in autoimmune diseases, this leads to unwanted and persistent inflammation. Anti-TNF-α monoclonal antibodies have revolutionized the therapy of these disorders by blocking TNF-α and preventing its binding to TNF-α receptors, thus suppressing the inflammation. Herein, we propose an alternative in the form of molecularly imprinted polymer nanogels (MIP-NGs). MIP-NGs are synthetic antibodies obtained by nanomoulding the 3-dimensional shape and chemical functionalities of a desired target in a synthetic polymer. Using an in-house developed in silico rational approach, epitope peptides of TNF-α were generated and 'synthetic peptide antibodies' were prepared. The resultant MIP-NGs bind the template peptide and recombinant TNF-α with high affinity and selectivity, and can block the binding of TNF-α to its receptor. Consequently they were applied to neutralize pro-inflammatory TNF-α in the supernatant of human THP-1 macrophages, leading to a downregulation of the secretion of pro-inflammatory cytokines. Our results suggest that MIP-NGs, which are thermally and biochemically more stable and easier to manufacture than antibodies, and cost-effective, are very promising as next generation TNF-α inhibitors for the treatment of inflammatory diseases.

Topological Data Analysis and Use of Mapper for Cerebral Aneurysm Rupture Status Discrimination Based on 3-Dimensional Shape Analysis.

Topological data analysis (TDA), which identifies patterns in data through simplified topological signatures, has yet to be applied to aneurysm research. We investigate TDA Mapper graphs (Mapper) for aneurysm rupture discrimination. Two hundred sixteen bifurcation aneurysms (90 ruptured) from 3-dimensional rotational angiography were segmented from vasculature and evaluated for 12 size/shape and 18 enhanced radiomics features. Using Mapper, uniformly dense aneurysm models were represented as graph structures and described by graph shape metrics. Mapper dissimilarity scores (MDS) were computed between pairs of aneurysms based on shape metrics. Lower MDS described similar shapes, whereas high MDS represented shapes that do not share common characteristics. Ruptured/unruptured average MDS scores (how "far" an aneurysm is shape-wise to ruptured/unruptured data sets, respectively) were evaluated for each aneurysm. Rupture status discrimination univariate and multivariate statistics were reported for all features. The average MDS for pairs of ruptured aneurysms were significantly larger compared with unruptured pairs (0.055 ± 0.027 vs 0.039 ± 0.015, P < .0001). Low MDS suggest that, in contrast to ruptured aneurysms, unruptured aneurysms have similar shape characteristics. An MDS threshold value of 0.0417 (area under the curve [AUC] = 0.73, 80% specificity, 60% sensitivity) was identified for rupture status classification. Under this predictive model, MDS scores <0.0417 would identify unruptured status. MDS statistical performance in discriminating rupture status was similar to that of nonsphericity and radiomics Flatness (AUC = 0.73), outperforming other features. Ruptured aneurysms were more elongated ( P < .0001), flatter ( P < .0001), and showed higher nonsphericity ( P < .0001) compared with unruptured. Including MDS in multivariate analysis resulted in AUC = 0.82, outperforming multivariate analysis on size/shape (AUC = 0.76) and enhanced radiomics (AUC = 0.78) alone. A novel application of Mapper TDA was proposed for aneurysm evaluation, with promising results for rupture status classification. Multivariate analysis incorporating Mapper resulted in high accuracy, which is particularly important given that bifurcation aneurysms are challenging to classify morphologically. This proof-of-concept study warrants future investigation into optimizing Mapper functionality for aneurysm research.

COMPARISON AND EVALUATION OF TLSS AND MOBILE LIDAR SCANNERS FOR MULTI-SCALE 3D DOCUMENTATION OF CULTURAL HERITAGE

Abstract. Field documentation of archaeological sites and built heritage by point-cloud is very effective in yielding arbitrary 3- dimensional shapes of various objects. There are several options for methods and devices such as TLSs, Mobile scanners, and SfM-MVS-based CRP. These are based on different technology, mechanism, and algorithm. Therefore, it is difficult to compare their precision, resolution, and measurement error in the same manner under practical conditions rather than catalog specification. The authors plan and carry out a field experiment of 3D point-cloud measurement in an archaeological site to obtain sample data for comparison between methods and devices under controlled conditions. Through comparative analysis, we evaluate character and advantage, as well as comparative measurement errors between methods and devices, then argue the combined use of different methods and devices under the “Multi-scale 3D scan strategy”.

Post-pubertal developmental trajectories of laryngeal shape and size in humans

Laryngeal morphotypes have been hypothesized related to both phonation and to laryngeal pathologies. Morphotypes have not been validated or demonstrated quantitatively and sources of shape and size variation are incompletely understood but are critical for the explanation of behavioral changes (e.g., changes of physical properties of a voice) and for therapeutic approaches to the larynx. This is the first study to take this crucial step and results are likely to have implications for surgeons and speech language pathologists. A stratified human sample was interrogated for phenotypic variation of the vocal organ. First, computed tomography image stacks were used to generate three-dimensional reconstructions of the thyroid cartilage. Then cartilage shapes were quantified using multivariate statistical analysis of high dimensional shape data from margins and surfaces of the thyroid cartilage. The effects of sex, age, body mass index (BMI) and body height on size and shape differences were analyzed. We found that sex, age, BMI and the age–sex interaction showed significant effects on the mixed sex sample. Among males, only age showed a strong effect. The thyroid cartilage increased in overall size, and the angulation between left and right lamina decreased in older males. Age, BMI and the age–height interaction were statistically significant factors within females. The angulation between left and right lamina increased in older females and was smaller in females with greater BMI. A cluster analysis confirmed the strong age effect on larynx shape in males and a complex interaction between the age, BMI and height variables in the female sample. The investigation demonstrated that age and BMI, two risk factors in a range of clinical conditions, are associated with shape and size variation of the human larynx. The effects influence shape differently in female and male larynges. The male–female shape dichotomy is partly size-dependent but predominantly size-independent.

Three Dimensional Shape Reconstruction via Polarization Imaging and Deep Learning

Deep-learning-based polarization 3D imaging techniques, which train networks in a data-driven manner, are capable of estimating a target’s surface normal distribution under passive lighting conditions. However, existing methods have limitations in restoring target texture details and accurately estimating surface normals. Information loss can occur in the fine-textured areas of the target during the reconstruction process, which can result in inaccurate normal estimation and reduce the overall reconstruction accuracy. The proposed method enables extraction of more comprehensive information, mitigates the loss of texture information during object reconstruction, enhances the accuracy of surface normal estimation, and facilitates more comprehensive and precise reconstruction of objects. The proposed networks optimize the polarization representation input by utilizing the Stokes-vector-based parameter, in addition to separated specular and diffuse reflection components. This approach reduces the impact of background noise, extracts more relevant polarization features of the target, and provides more accurate cues for restoration of surface normals. Experiments are performed using both the DeepSfP dataset and newly collected data. The results show that the proposed model can provide more accurate surface normal estimates. Compared to the UNet architecture-based method, the mean angular error is reduced by 19%, calculation time is reduced by 62%, and the model size is reduced by 11%.

Impact Analysis of Compression Parameters and Fracture of Gummy Candy Using the Mastication Process Simulator

Masticatory devices are expected to elucidate the relationship between changes in physical properties of food during mastication and masticatory parameters. This study analyzed the relationship between the progression of gummy candy fracture and some parameters that can be controlled by a novel mastication process simulator named “ORAL-MAPS”. Three commercial gummy candies were selected as test materials. The gummy candies were processed under the following 18 conditions: compression force (200, 400, and 600 N), compression frequency (30 and 60 cycles/min), and number of compressions (20, 40, and 60 cycles). Thereafter, the mean volume of the bolus particles was calculated utilizing a 3-dimensional shape measuring machine and used as an indicator of fracture progression. The result of multiple regression analysis with the logarithm of mean particle volume as the objective variable and device parameters as the explanatory variables showed that the number and frequency of compressions markedly affected the fracture of all gummy candies, whereas compression force exerted a smaller effect than these. Therefore, the standardized partial regression coefficient could be used to evaluate the characteristics of the gummy candies.

The Corticosteroid Meniscectomy Trial of Extended-Release Triamcinolone Injection After Arthroscopic Partial Meniscectomy: Protocol for a Double-Blind Randomized Controlled Trial.

Meniscal tear in older adults often accompanies knee osteoarthritis and is commonly treated with arthroscopic partial meniscectomy (APM) when patients have persistent pain after a trial of physical therapy. Cross-sectional evidence suggests that synovitis is associated with baseline pain in this patient population, but little is known about the relationship between synovitis and postoperative recovery or progression of knee osteoarthritis. Intra-articular extended-release triamcinolone may reduce inflammation and thereby improve outcomes and slow disease progression. This article presents the rationale behind the Corticosteroid Meniscectomy Trial (CoMeT) and describes its study design and implementation strategies. Randomized controlled trial. CoMeT is a 2-arm, 3-center, randomized placebo-controlled trial designed to establish the clinical efficacy of extended-release triamcinolone administered via intra-articular injection immediately after APM. The primary outcome is change in Knee injury and Osteoarthritis Outcome Score Pain subscore at 3-month follow-up. Synovial biopsy, joint fluid aspirate, and urine and blood sample analyses will examine the associations between various objective measures of baseline inflammation and pre- and postoperative outcome measures and clinical responses to triamcinolone intervention. Quantitative 3-T magnetic resonance imaging will evaluate cartilage and meniscal composition and 3-dimensional bone shape to detect early joint degeneration. We discuss methodologic innovations and challenges. To our knowledge, this is the first randomized double-blind clinical trial that will analyze the effect of extended-release triamcinolone acetonide on pain, magnetic resonance imaging measures of structural change and effusion/synovitis, soluble biomarkers, and synovial tissue transcriptomics after APM.

3-Dimensional Topology in Materials Science

In this article, we discuss applications of topology to materials science. We explain how topology is used to express the structure and shape of materials. The structure of a multicyclic polymer is described by applying graph theory and the 3-dimensional shape is described by applying knot theory. Polycontinuous patterns of microphase separation of a block copolymer melt can be analyzed by applying the 3-dimensional topology. We also discuss the topological structures of interlocking molecules composed of metal-peptide rings using the concept of polyhedral links.

Encoding 3D Point Contexts for Self-Supervised Spall Classification Using 3D Bridge Point Clouds

Geometric features, such as normal and curvature, have been prominently used for point cloud-based unsupervised spall classification. In addition, some researchers use hand-crafted features (e.g., out-of-plane distance, eccentricity, principal curvatures in 2D slices). These features perform well in low noise settings; however, the performance tapers down significantly when the quality of point clouds is affected by factors such as higher noise and inconsistent point-to-point spacing. Instead of relying purely on handcrafted features, the research presented in this paper investigates the potential for combining domain knowledge with deep learning to automatically learn better quality defect-sensitive features for point cloud-based spall classification. Specifically, generic three dimensional (3D) shape and 3D neighborhood features have been encoded as inputs to a deep autoencoder network for self-supervised spall classification from point clouds. Overall, this approach only resulted in marginal improvement over classification results from the current state-of-the-art unsupervised approach that uses handcrafted features. However, significant improvement in the results were observed in datasets that had higher noise levels. Given that noise is pervasive in datasets from outdoor settings like civil infrastructure, this added robustness to noise improves the reliability of point cloud-based condition assessment for concrete bridges.