Shape Deformation Research Articles

Self-equilibrium, stability, and accuracy degradation of imperfect prismatic tensegrities with additional cable nets

Prismatic tensegrity is an attractive structure due to the unique advantages of self-equilibrium, lightweight, small compression volume, specific super stability, ease of deployment, and functional extension. However, it is subject to geometric large deformation when suffering from imperfect factors like manufacturing errors, thermal deformation, slips of tensioning knots, and mechanical relaxation of members, which will eventually reduce the accuracy and reliability. We develop a kind of prismatic tensegrities with additional cable nets for space deployable constructions. For the specific requirements of high accuracy and high reliability, we fully consider the influence of imperfect factors, and explore their environment- and time-dependent characteristics from the view of self-equilibrium, stability, and accuracy. First, a preload optimization method is established for high-accuracy determination of the shape and uniform distribution of preload. Then, a high-fault-tolerance accuracy estimation method is proposed for accuracy degradation research, for which nonlinear force density equations and time-dependent tangential stiffness matrix are formulated to obtain deformation shapes and stability criteria beforehand. Eventually, the characteristics of the prismatic tensegrities with additional cable nets, under the influence of imperfect factors, are fully revealed and forecasted. The methods and some conclusions of this study can also be applied to other complex tensegrities or prestressed pin-jointed systems.

CFD simulation of a small bubble motion in 3D flow domain: effect of liquid density, viscosity and surface tension

The motion of spherical and slightly deformed bubbles rising rectilinearly in a stagnant liquid was numerically simulated in a fully three-dimensional domain using the CFD solver COMSOL Multiphysics. The interface was tracked by the built-in conservative Level set method on a fixed numerical grid. The purpose of this work was to study the single bubble motion in three industrially used liquids that differ significantly in surface tension, density, and viscosity. The motion of bubbles with diameters up to 1.6 mm was also studied experimentally using a high-speed camera. The data obtained together with the results of theoretical models for bubble motion were used for the validation of the simulation data. Using a 3D domain, very good agreement was obtained in both bubble shape deformations and bubble terminal velocity. The best results were achieved for propanol with low surface tension and low viscosity. In the case of high surface tension and low viscosity liquid (water), both the bubble deformation and the bubble velocity were slightly underestimated. In the case of glycerol (high surface tension and viscosity), the negligible bubble deformation is correctly calculated, but the velocity is again slightly underestimated.Graphical abstract

Systematic atomic structure datasets for machine learning potentials: Application to defects in magnesium

We present a physically motivated strategy for the construction of training sets for transferable machine learning interatomic potentials. It is based on a systematic exploration of all possible space groups in random crystal structures, together with deformations of cell shape, size, and atomic positions. The resulting potentials turn out to be unbiased and generically applicable to studies of bulk defects without including any defect structures in the training set or employing any additional active learning. Using this approach we construct transferable potentials for pure magnesium that reproduce the properties of hexagonal closed packed (hcp) and body centered cubic (bcc) polymorphs very well. In the process we investigate how different types of training structures impact the properties and the predictive power of the resulting potential.

Quantum singularities

Two spatial regions $B$ and $R$ are hyperentangled if the generalized entropy satisfies $S_{\text{gen}}^{B\cup R}<S_{\text{gen}}^R$. If in addition all future (or all past) directed inward null shape deformations of $B$ decrease $S_{\text{gen}}^{B\cup R}$, then we show that the causal development of $B$, with $R$ held fixed, must be incomplete. This result eliminates the Null Energy Condition from the assumptions of a recently proven singularity theorem. Instead, we assume a quantum version of the Bousso bound. Taking $R$ to contain the Hawking radiation after the Page time, our theorem predicts a singularity in the past causal development of the black hole interior. This is surprising because the classical spacetime is nonsingular in the past. However, one finds that Cauchy slices that are required to contain $R$ do not remain in the semiclassical regime. The quantum singularities predicted by our theorem are an obstruction to further semiclassical evolution, generalizing the singularities of classical general relativity.

4D printed TMP origami metamaterials with programmable mechanical properties

Origami structures have received significant attention in engineering due to their unique design methods and deformation modes along the crease lines. However, the configurations of origami structures are fixed after fabrication, and their mechanical properties are tough to tune and adapt. To overcome this limitation, herein, a 4D printing method is employed to develop Tachi-Miura Polyhedron (TMP) origami metamaterials with reconfigurable shapes and programmable mechanical properties. The elastic modulus, folding/unfolding deformations, force-displacement curves and Poisson's ratios of TMP origami metamaterials under different structure parameters and temperature are studied through theoretical models, finite element analysis (FEA) and experiments. Results reveal that the TMP origami structures can achieve negative Poisson's ratios, and their deformation modes can switch between monostable and bistable states. Due to shape memory effects, deformation shapes and mechanical properties can be programmed by controlling temperature and tuning structural parameters. The 4D Printed origami metamaterials can provide practical solutions in various engineering applications such as self-deployable structures, energy absorption devices and flexible electronics.

CA-CGNet: Component-Aware Capsule Graph Neural Network for Non-Rigid Shape Correspondence

3D non-rigid shape correspondence is significant but challenging in computer graphics, computer vision, and related fields. Although some deep neural networks have achieved encouraging results in shape correspondence, due to the complexity of the local deformation of non-rigid shapes, the ability of these networks to identify the spatial changes of objects is still insufficient. In this paper, we design a Component-aware Capsule Graph Network (CA-CGNet) to further address the features of embedding space based on the component constraints. Specifically, the dynamic clustering strategy is used to classify the features of patches produced by over-segmentation in order to further reduce noise interference. Moreover, aiming at the problem that existing routing ignores the embedding relationship between capsules, we propose a component-aware capsule graph routing to fully describe the relationship between capsules, which regards capsules as nodes in the graph network and constrains nodes through component information. Then, a knowledge distillation strategy is introduced to improve the convergence speed of the network by decreasing the parameters while maintaining accuracy. Finally, a component pair constraint is added to the functional map, and the component-based semantic loss function is proposed, which can compute isomeric in both direct and symmetric directions. The experimental results show that CA-CGNet improves by 10.21% compared with other methods, indicating the accuracy, generalization, and efficiency of our method on the FAUST, SCAPE, TOSCA, and KIDS datasets.

Self-Reconfigurable Soft-Rigid Mobile Agent With Variable Stiffness and Adaptive Morphology

In this letter, we propose a novel design of a hybrid mobile robot with controllable stiffness and deformable shape. Compared to conventional mobile agents, our system can switch between rigid and compliant phases by solidifying or melting Field's metal in its structure and, thus, alter the shape through the motion of its active components. In the soft state, the robot's main body can bend into circular arcs, which enables it to conform to surrounding curved objects. This variable geometry of the robot creates new motion modes which cannot be described by standard (i.e., fixed geometry) models. To this end, we develop a unified mathematical model that captures the differential kinematics of both rigid and soft states. An optimised control strategy is further proposed to select the most appropriate phase states and motion modes needed to reach a target pose-shape configuration. The performance of our new method is validated with numerical simulations and experiments conducted on a prototype system.

Electrical Impedance Tomographic Shape Sensing for Soft Robots

With infinite degrees of freedom, soft robots are expected to achieve dexterous and complex tasks, but this also puts forward higher requirements for their sensing capabilities. An important sensing task in soft robots is sensing their own deformation and current shape. Currently, most of the existing soft shaping sensors are limited by local perception abilities, stretchability, and fabrication difficulties. We propose a sensing method based on Electrical Impedance Tomography (EIT), which reconstructs conductivity patterns distributed on a surface, by considering the deformation-caused resistance changes. Comparison between the theoretical and experimental patterns reveals that even though the quality of the pattern is affected by a large amount of noise, the considered features are still able to reflect the change of shape. With the help of neural networks, the pattern is decoded to the physical data related to the deformation. Detection of the planar shape changes and proprioception of a sensor-integrated soft robot are presented to exhibit the capability of our method. Results show that the detected error ratios are mostly under 5% and 3% for 2D and 3D conditions respectively.

Local variations of charge radii for nuclei with even Z from 84 to 120

Pronounced changes of nuclear charge radii provide a stringent benchmark on the theoretical models and play a vital role in recognizing various nuclear phenomena. In this work, the systematic evolutions of nuclear charge radii along even Z = 84–120 isotopic chains are first investigated by the recently developed new ansatz under the covariant density functional. The calculated results show that the shell closure effects of nuclear charge radii appear remarkably at the neutron numbers N = 126 and 184. Interestingly, the arch-like shapes of charge radii between these two strong neutron-closed shells are naturally observed. Across the N = 184 shell closure, the abrupt increase in charge radii is still evidently emerged. In addition, the rapid raise of nuclear charge radii from the neutron numbers N = 138 to N = 144 is disclosed clearly in superheavy regions due to the enhanced shape deformation.

Keeping Deep Lithography Simulators Updated: Global–Local Shape-Based Novelty Detection and Active Learning

Learning-based presimulation (i.e., layout-to-fabrication) models have been proposed to predict the fabrication-induced shape deformation from an IC layout to its fabricated circuit. Such models are usually driven by pairwise learning, involving a training set of layout patterns and their reference shape images after fabrication. However, it is expensive and time consuming to collect the reference shape images of all layout clips for model training and updating. To address the problem, we propose a deep-learning-based layout novelty detection scheme to identify novel (unseen) layout patterns, which cannot be well predicted by a pretrained presimulation model. We devise a global–local novelty scoring mechanism to assess the potential novelty of a layout by exploiting two subnetworks: 1) an autoencoder and 2) a pretrained presimulation model. The former characterizes the global structural dissimilarity between a given layout and training samples, whereas the latter extracts a latent code representing the fabrication-induced local deformation. By integrating the global dissimilarity with the local deformation boosted by a self-attention mechanism, our model can accurately detect novelties without the ground-truth circuit shapes of test samples. Based on the detected novelties, we further propose two active-learning strategies to sample a reduced amount of representative layouts most worthy to be fabricated for acquiring their ground-truth circuit shapes. Experimental results demonstrate: 1) the effectiveness of our layout novelty detection algorithm and 2) the ability of our active-learning strategies in selecting representative novel layouts for keeping a learning-based presimulation model updated.

Superadditivity and Convex Optimization for Globally Optimal Cell Segmentation Using Deformable Shape Models.

Cell nuclei segmentation is challenging due to shape variation and closely clustered or partially overlapping objects. Most previous methods are not globally optimal, limited to elliptical models, or are computationally expensive. In this work, we introduce a globally optimal approach based on deformable shape models and global energy minimization for cell nuclei segmentation and cluster splitting. We propose an implicit parameterization of deformable shape models and show that it leads to a convex energy. Convex energy minimization yields the global solution independently of the initialization, is fast, and robust. To jointly perform cell nuclei segmentation and cluster splitting, we developed a novel iterative global energy minimization method, which leverages the inherent property of superadditivity of the convex energy. This property exploits the lower bound of the energy of the union of the models and improves the computational efficiency. Our method provably determines a solution close to global optimality. In addition, we derive a closed-form solution of the proposed global minimization based on the superadditivity property for non-clustered cell nuclei. We evaluated our method using fluorescence microscopy images of five different cell types comprising various challenges, and performed a quantitative comparison with previous methods. Our method achieved state-of-the-art or improved performance.

Structural brain changes in patients with post-COVID fatigue: a prospective observational study

Post-COVID syndrome is a severe long-term complication of COVID-19. Although fatigue and cognitive complaints are the most prominent symptoms, it is unclear whether they have structural correlates in the brain. We therefore explored the clinical characteristics of post-COVID fatigue, describe associated structural imaging changes, and determine what influences fatigue severity. We prospectively recruited 50 patients from neurological post-COVID outpatient clinics (age 18-69 years, 39f/8m) and matched non-COVID healthy controls between April 15 and December 31, 2021. Assessments included diffusion and volumetric MR imaging, neuropsychiatric, and cognitive testing. At 7.5 months (median, IQR 6.5-9.2) after the acute SARS-CoV-2 infection, moderate or severe fatigue was identified in 47/50 patients with post-COVID syndrome who were included in the analyses. As a clinical control group, we included 47 matched multiple sclerosis patients with fatigue. Our diffusion imaging analyses revealed aberrant fractional anisotropy of the thalamus. Diffusion markers correlated with fatigue severity, such as physical fatigue, fatigue-related impairment in everyday life (Bell score) and daytime sleepiness. Moreover, we observed shape deformations and decreased volumes of the left thalamus, putamen, and pallidum. These overlapped with the more extensive subcortical changes in MS and were associated with impaired short-term memory. While fatigue severity was not related to COVID-19 disease courses (6/47 hospitalised, 2/47 with ICU treatment), post-acute sleep quality and depressiveness emerged as associated factors and were accompanied by increased levels of anxiety and daytime sleepiness. Characteristic structural imaging changes of the thalamus and basal ganglia underlie the persistent fatigue experienced by patients with post-COVID syndrome. Evidence for pathological changes to these subcortical motor and cognitive hubs provides a key to the understanding of post-COVID fatigue and related neuropsychiatric complications. Deutsche Forschungsgemeinschaft (DFG) and German Ministry of Education and Research (BMBF).

Lignin precipitation-driven fabrication of gradient porous hydrogel actuator with temperature response

An operative and straightforward precipitation-driven approach was reported to fabricate an anisotropic hydrogel actuator with temperature response. Through in situ deposition of lignin nanoparticles (LNP) in the process of polyacrylamide (PAM) polymerization with the presence of hydroxypropyl cellulose (HPC), an inhomogeneous hydrogel network (PAM/HPC/lignin hydrogel, PHL hydrogel) with distinct gradient porous structure was achieved that could be tailored to form a hydrogel actuator. The PHL hydrogels exhibit faster shape deformation as responding to temperature and higher mechanical properties caused by introducing the LNP and HPC chains. The deformation direction and rate of the hydrogel actuator could be influenced by the lignin content, temperature, and as well as their shape. The maximum bending angle could reach near 360° with 60 s as it was exposed to 60 °C. Due to the excellent bending behavior of the PHL hydrogel, the potential applications as grippers and valves were studied, and the results showed its sensitive response to temperature, suggesting its potential application as an intelligent actuator in the future.

Analysis of Diabetic Foot Deformation and Plantar Pressure Distribution of Women at Different Walking Speeds.

Official guidelines state that suitable physical activity is recommended for patients with diabetes mellitus. However, since walking at a rapid pace could be associated with increased plantar pressure and potential foot pain, the footwear condition is particularly important for optimal foot protection in order to reduce the risk of tissue injury and ulceration of diabetic patients. This study aims to analyze foot deformation and plantar pressure distribution at three different walking speeds (slow, normal, and fast walking) in dynamic situations. The dynamic foot shape of 19 female diabetic patients at three walking speeds is obtained by using a novel 4D foot scanning system. Their plantar pressure distributions at the three walking speeds are also measured by using the Pedar in-shoe system. The pressure changes in the toes, metatarsal heads, medial and lateral midfoot, and heel areas are systematically investigated. Although a faster walking speed shows slightly larger foot measurements than the two other walking speeds, the difference is insignificant. The foot measurement changes at the forefoot and heel areas, such as the toe angles and heel width, are found to increase more readily than the measurements at the midfoot. The mean peak plantar pressure shows a significant increase at a faster walking speed with the exception of the midfoot, especially at the forefoot and heel areas. However, the pressure time integral decreases for all of the foot regions with an increase in walking speed. Suitable offloading devices are essential for diabetic patients, particularly during brisk walking. Design features such as medial arch support, wide toe box, and suitable insole material for specific area of the foot (such as polyurethane for forefoot area and ethylene-vinyl acetate for heel area) are essential for diabetic insole/footwear to provide optimal fit and offloading. The findings contribute to enhancing the understanding of foot shape deformation and plantar pressure changes during dynamic situations, thus facilitating the design of footwear/insoles with optimal fit, wear comfort, and foot protection for diabetic patients.

Microstructural Evolution and Mechanical Performance of Two Joints of Medium-Mn Stainless Steel with Low- and High-Alloyed Steels

In this work, 2 mm thick medium-Mn austenitic stainless steel (MMn-SS) plates were joined with austenitic NiCr stainless steel (NiCr-SS) and low-carbon steel (LCS) using the gas tungsten arc welding technique. A precise adjustment of the welding process parameters was conducted to achieve high-quality dissimilar joints of MMn-SS with NiCr-SS and LCS. The microstructural evolution was studied using laser scanning confocal and electron microscopes. Secondary electron imaging and electron backscatter diffraction (EBSD) techniques were intensively employed to analyze the fine features of the weld structures. The mechanical properties of the joints were evaluated by uniaxial tensile tests and micro-indentation hardness (HIT). The microstructure of the fusion zone (FZ) in the MMn-SS joints exhibited an austenitic matrix with a small fraction of δ-ferrite, ~6%. The tensile strength (TS) of the MMn-SS/NiCr-SS joint is significantly higher than that of the MMn-SS/LCS joint. For instance, the TSs of MMn-SS joints with NiCr-SS and LCS are 610 and 340 MPa, respectively. The tensile properties of MMn-SS/LCS joints are similar to those of BM LCS, since the deformation behavior and shape of the tensile flow curve for that joint are comparable with the flow curve of LCS. The HIT measurements show that the MMn-SS/NiCr-SS joint is significantly stronger than the MMn-SS/LCS joint since the HIT values are 2.18 and 1.85 GPa, respectively.

Evaluation of expansion forces of five pure bone-borne maxillary expander designs anchored with orthodontic mini-implants: An in vitro study.

This in vitro study investigates the limit of expansion forces and torque wrench forces developed by five skeletal bone expander designs (MICRO 2/4 expanders™) for clinical application. A total of 30 skeletal expanders were placed in artificial bone blocks and mechanically tested, simulating maxillary expansion. Differences in jackscrew (Dentaurum™ [D], Superscrew™ [S] and Powerscrew™ [P]), number of orthodontic mini-implants (OMIs; two or four) and their placement inclinations (parallel 0° or 10° inclination) form five designs (D4/10°, S4/0°, S4/10°, P4/10° and P2/10°). Expansion forces and torque wrench values were registered, and radiographs were made initially and after 4 mm of expansion. Stress-strain curves were obtained after successive activations and the statistical analysis was performed as appropriate. Plastic deformations in the OMIs and jackscrew occurred around the activation numbers 11-13, with torque wrench values in the range of 500-700 cN. The maximum expansion forces in expanders with four OMIs varied from 93.0 (D4/10°) to 166.6 N (P4/10°) whereas two OMI expanders (P2/10°) registered forces of 79.4 N. Radiographs revealed during loads bending forces (S4/00°, S4/10°) with jackscrew and OMIs deformation in a convex shape, and shear forces (P4/10°, P2/10°) demonstrated only OMIs deformation in a concave shape, providing 15% more expansive force. The jackscrew D4/10° did not have any deformation, but its wire key did not allow reliable activations from activation number 10 and compared to S4/10° and P4/10°, these expanders provided greater expansion forces (P = 0.000 and P = 0.032, respectively). The different results obtained in stability and expansion forces indicate that if the activations are carried out under extreme conditions, they may have clinical importance with deformations and non-working expansion mechanics. Jackscrew designs play an important role in expansive forces and expander stability. Torque wrench values can be used clinically as a tool to asses the expansion forces and to avoid deformations.

Bilateral ankle deformities affects gait kinematics in chronic stroke patients.

Stroke patients suffer from ankle joint deformities due to spastic ankle muscles. This study evaluated the viability of using 3D scanned surface images of the feet of stroke victims to visually assess the deformities of a hemiparetic foot and investigated the influences of deformed ankle joints on gait kinematics. A total of 30 subjects with stroke-induced hemiparesis and 11 age-matched healthy controls completed the clinical assessments. We analyzed their feet's morphometric characteristics using a 3D scanner, identified convenient anthropometric measurements, and conducted gait trials on even and uneven terrains. The 3D foot morphometric characteristics were evaluated using the geometric morphometrics method (GMM). Results showed that there were significant differences in bilateral foot shapes between the chronic stroke patients and healthy controls and between the paretic and non-paretic sides in the chronic stroke patients. In stroke patients, those with the smaller medial malleoli's vertical tilt angles showed significantly different ankle ranges of motion of dorsi-/plantar flexion during gaits on uneven terrains (p = 0.009). In addition, those with the greater medial malleoli's vertical tilt angles showed significantly different ankle ranges of motion of inversion/eversion during gaits on even and uneven terrains (p < 0.05). Using 3D scanning technology, bilateral morphometric changes in the feet of chronic stroke patients were shown by GMM and the simple anthropometric measurements identified its shape deformities in the feet. Their possible effects on gait kinematics while walking on uneven terrains were investigated. Current methodology can be potentially useful in applying conventional productions of clinically manufactured, patient-fitted ankle-foot-orthosis in orthotics and prosthetics, and in detecting various unidentified pathological deformities in the feet.

Multi-level contour combination features for shape recognition

We present a novel multi-level contour combination feature for shape recognition. This combination feature effectively solves large intra-class changes and nonlinear deformations of object shapes, thereby enhancing the performance of shape recognition. First, we divide the shape contour into two levels: the sampling points and the contour fragments, where sampling points are used to describe the detailed information of a shape and contour fragments are used to represent the global feature of a shape. Second, we employ the Fisher vector (FV) approach to encode the local sampling point feature and contour fragment feature as high-level characteristics. Finally, we combine the high-level characteristics after FV encoding and perform shape recognition through a linear support vector machine (SVM) model. The proposed method has been assessed on three benchmark shape datasets, including the Animal, MPEG-7,and ETH-80 datasets. Our method achieves 92.70%, 99.26% and 98.32% classification accuracy on the Animal, MPEG-7, and ETH-80 datasets, respectively. In addition, our method can also be applied to the classification of objects in real-word scenes. We combine the Weizmann Horse and the ETHZ Cow real-world scene datasets, and our method achieves 99.25% classification accuracy on the combined dataset. The recognition results of our approach are better than prior state-of-the-art shape recognition methods, which demonstrate the effectiveness and superiority of our approach.

Dependence of protein-induced lipid bilayer deformations on protein shape.

Membrane proteins typically deform the surrounding lipid bilayer membrane, which can play an important role in the function, regulation, and organization of membrane proteins. Membrane elasticity theory provides a beautiful description of protein-induced lipid bilayer deformations, in which all physical parameters can be directly determined from experiments. While analytic solutions of protein-induced elastic bilayer deformations are most easily developed for proteins with approximately circular cross sections, structural biology has shown that membrane proteins come in a variety of distinct shapes, with often considerable deviations from a circular cross section. We develop here a boundary value method (BVM) that permits the construction of analytic solutions of protein-induced elastic bilayer deformations for protein shapes with arbitrarily large deviations from a circular cross section, for constant as well as variable boundary conditions along the bilayer-protein interface. We apply this BVM to protein-induced lipid bilayer thickness deformations. Our BVM reproduces available analytic solutions for proteins with circular cross sectionand yields, for proteins with noncircular cross section, excellent agreement with numerical, finite element solutions. On this basis, we formulate a simple analytic approximation of the bilayer thickness deformation energy associated with general protein shapes and show that, for modest deviations from rotational symmetry, this analytic approximation is in good agreement with BVM solutions. Using the BVM, we survey the dependence of protein-induced elastic bilayer thickness deformations on protein shape, and thus explore how the coupling of protein shape and bilayer thickness deformations affects protein oligomerization and transitions in protein conformational state.

A non-manufacturer-sponsored, retrospective study to assess 2-year safety outcomes of the BellaGel® SmoothFine as compared with its competitors in the context of the first Korean case of a medical device fraud.

We conducted this study to assess preliminary 2-year safety outcomes of an implant-based augmentation mammaplasty using the BellaGel® SmoothFine in the context of the first Korean case of a medical device fraud. Our clinical series of the patients (n = 579; 1,158 breasts) received augmentation using the BellaGel® SmoothFine, Naturgel™, Motiva Ergonomix™, Eurosilicone Round Collection™, Natrelle® INSPIRA™, Natrelle® 410, Mentor® MemoryGel Xtra or Microthane®. The patients were evaluated for incidences of postoperative complications and Kaplan-Meier survival and hazards. Overall, there were a total of 101 cases (17.4%) of postoperative complications; these include 31 cases (5.4%) of shape deformity, 21 cases (3.6%) of CC, 18 cases (3.1%) of early seroma, 8 cases (1.4%) of infection, 5 cases (0.9%) of early hematoma, 1 case (0.2%) of delayed hematoma, 1 case (0.2%) of rupture and 1 case (0.2%) of ripping. Moreover, there were also 15 cases (2.6%) of other complications. There were significant differences in incidences of postoperative complications between the breast implants from different manufacturers (P = 0.034). The Natrelle® 410 showed the longest survival (333.3±268.2 [141.5-525.1] days). A subgroup analysis showed that there were no significant differences in incidences of postoperative complications between the breast implants (P = 0.831). Moreover, the Natrelle® INSPIRA™ showed the longest survival (223.7±107.1 [-42.3-489.6] days). Here, we describe preliminary 2-year safety outcomes of an implant-based augmentation mammaplasty using the BellaGel® SmoothFine in the context of the first Korean case of a medical device fraud.