Uncovering the dual role of dimensionless radius in buckling of spherical shells with random geometric imperfections.

Localized deformation and randomly shaped imperfections are salient features of buckling-type instabilities in thin-walled load-bearing structures. However, it is generally agreed that their complex interactions in response to mechanical loading are not yet sufficiently understood, as evidenced by buckling-induced catastrophic failures which continue to today. This study investigates how the intimate coupling between localization mechanisms and geometric imperfections combine to determine the statistics of the pressure required to buckle (the illustrative example of) a hemispherical shell. The geometric imperfections, in the form of a surface, are defined by a random field generated over the nominally hemispherical shell geometry, and the probability distribution of the buckling pressure is computed via stochastic finite element analysis. Monte-Carlo simulations are performed for a wide range of the shell's radius to thickness ratio, as well as the correlation length of the spatial distribution of the imperfection. The results show that over this range, the buckling pressure is captured by the Weibull distribution. In addition, the analyses of the deformation patterns observed during the simulations provide insights into the effects of certain characteristic lengths on the local buckling that triggers global instability. In light of the simulation results, a probabilistic model is developed for the statistics of the buckling load that reveals how the dimensionless radius plays a dual role which remained hidden in previous deterministic analyses. The implications of the present model for reliability-based design of shell structures are discussed.

Experimentally probing the stability of thin-shell structures under pure bending.

This paper studies the stability of space structures consisting of longitudinal, open-section thin-shells transversely connected by thin rods subjected to a pure bending moment. Localization of deformation, which plays a paramount role in the nonlinear post-buckling regime of these structures and is extremely sensitive to imperfections, is investigated through probing experiments. As the structures are bent, a probe locally displaces the edge of the thin shells, creating local dimple imperfections. The range of moments for which the early buckling of the structures can be triggered by this perturbation is determined, as well as the energy barrier separating the pre-buckling and post-buckling states. The stability of the local buckling mode is then illustrated by a stability landscape, and probing is extended to the entire structure to reveal alternate buckling modes disconnected from the structure's fundamental path. These results can be used to formulate efficient buckling criteria and pave the way to operating these structures close to their buckling limits, and even in their post-buckling regime, therefore significantly reducing their mass. This article is part of the theme issue 'Probing and dynamics of shock sensitive shells'.

Retarding the Shuttling Ions in the Electrochromic TiO2 with Extensive Crystallographic Imperfections.

To understand the role of structure imperfections on the performance of electrochromic transition metal oxide (ETMO) is challenging for the design of efficient smart windows. Herein, we investigate the performance evolution with tunable crystallographic imperfections for rutile TiO2 nanowire film (TNF). Structure imperfections, originating mainly from the copious oxygen deficiency, are apt to cumulatively retard the shuttling ions, resulting in the response rate for raw TNF being less than the half that of TNF annealed at 500 °C. We describe ion accommodation sites as a convolution of normal site and abnormal site, in which the normal site performs reversible coloration but the abnormal site contributes only to charge storage, which gives a rationale for the non-linear coloration and rate capability loss. These findings give a clear picture of the ion shuttling process, which is insightful for enhancing the electrochromic performance via structure reprogramming.

Standing Wave Binding of Hemispherical Resonator Containing First-Third Harmonics of Mass Imperfection under Linear Vibration Excitation.

Due to complicated processing technology, the mass distribution of a hemispherical resonator made of fused silica is not uniform, which can affect the azimuth of the standing wave of a resonator under the linear vibration excitation. Therefore, the analysis of standing wave evolution of a resonator with mass imperfection under linear vibration excitation is of significance for the improvement of the output accuracy of a gyroscope. In this paper, it is assumed that the resonator containing the first-third harmonics of mass imperfection is excited by horizontal and vertical linear vibration, respectively; then, the equations of motion of an imperfect resonator under the second-order vibration mode are established by the elastic thin shell theory and Lagrange mechanics principle. Through error mechanism analysis, it is found that, when the frequency of linear vibration is equal to the natural frequency of resonator, the standing wave is bound in the azimuth of different harmonics of mass imperfection with the change in vibration excitation direction. In other words, there are parasitic components in the azimuth of the standing wave of a resonator under linear vibration excitation, which can cause distortion of the output signal of a gyroscope. On the other hand, according to the standing wave binding phenomenon, the azimuths of the first-third harmonics of mass imperfection of a resonator can also be identified under linear vibration excitation, which can provide a theoretical method for the mass balance of an imperfect resonator.

Critical Roles of E2F3 in Growth and Musculo-skeletal Phenotype in Mice.

E2F3, a member of the E2F family, plays a critical role in cell cycle and proliferation by targeting downstream, retinoblastoma (RB) a tumor suppressor family protein. The purpose of this study, was to investigate the role and function of E2F3 in vivo. We examined phenotypic abnormalities, by deletion of the E2f3 gene in mice. Complete ablation of the E2F3 was fully penetrant, in the pure C57BL/6N background. The E2f3+/ - mouse embryo developed normally without fatal disorder. However, they exhibited reduced body weight, growth retardation, skeletal imperfection, and poor grip strength ability. Findings suggest that E2F3 has a pivotal role in muscle and bone development, and affect normal mouse growth.

Optimization of 1H decoupling eliminates sideband artifacts in 3D TROSY-based triple resonance experiments.

TROSY-based triple resonance experiments are essential for protein backbone assignment of large biomolecular systems by solution NMR spectroscopy. In a survey of the current Bruker pulse sequence library for TROSY-based experiments we found that several sequences were plagued by artifacts that affect spectral quality and hamper data analysis. Specifically, these experiments produce sidebands in the 13C(t 1) dimension with inverted phase corresponding to 1HN resonance frequencies, with approximately 5% intensity of the parent 13C crosspeaks. These artifacts originate from the modulation of the 1HN frequency onto the resonance frequency of 13Cα and/or 13Cß and are due to 180° pulses imperfections used for 1H decoupling during the 13C(t 1) evolution period. These sidebands can become severe for CAi, CAi-1 and/or CBi, CBi-1 correlation experiments such as TROSY-HNCACB. Here, we implement three alternative decoupling strategies that suppress these artifacts and, depending on the scheme employed, boost the sensitivity up to 14% on Bruker spectrometers. A class of comparable Agilent/Varian pulse sequences that use WALTZ16 1H decoupling can also be improved by this method resulting in up to 60-80% increase in sensitivity.

Correction of parallel transmission using concurrent RF and gradient field monitoring.

OBJECTIVES: The accuracy and precision of the parallel RF excitations are highly dependent on the spatial and temporal fidelity of the magnetic fields involved in spin excitation. The consistency between the nominal and effective fields is typically limited by the imperfections of the employed hardware existing both in the gradient system and the RF chain. In this work, we experimentally presented highly improved spatially tailored parallel excitations by turning the native hardware accuracy challenge into a measurement and control problem using an advanced field camera technology to fully correct parallel RF transmission experiment. MATERIALS AND METHODS: An array of NMR field probes is used to measure the multiple channel RF pulses and gradient waveforms recording the high power RF pulses simultaneously with low frequency gradient fields on equal time basis. The recorded waveforms were integrated in RF pulse design for gradient trajectory correction, time imperfection compensation and introduction of iterative RF pre-emphasis. RESULTS: Superior excitation accuracy was achieved. Two major applications were presented at 7 Tesla including multi-dimensional tailored RF pulses for spatially selective excitation and slice-selective spoke pulses for [Formula: see text] mitigation. CONCLUSION: Comprehensive field monitoring is a highly effective means of correcting for the field deviations during parallel transmit pulse design.

A comparative genomics approach revealed evolutionary dynamics of microsatellite imperfection and conservation in genus Gossypium.

BACKGROUND: Ongoing molecular processes in a cell could target microsatellites, a kind of repetitive DNA, owing to length variations and motif imperfection. Mutational mechanisms underlying such kind of genetic variations have been extensively investigated in diverse organisms. However, obscure impact of ploidization, an evolutionary process of genome content duplication prevails mostly in plants, on non-coding DNA is poorly understood. RESULTS: Genome sequences of diversely originated plant species were examined for genome-wide motif imperfection pattern, and various analytical tools were employed to canvass characteristic relationships among repeat density, imperfection and length of microsatellites. Moreover, comparative genomics approach aided in exploration of microsatellites conservation footprints in Gossypium evolution. Based on our results, motif imperfection in repeat length was found intricately related to genomic abundance of imperfect microsatellites among 13 genomes. Microsatellite decay estimation depicted slower decay of long motif repeats which led to predominant abundance of 5-nt repeat motif in Gossypium species. Short motif repeats exhibited rapid decay through the evolution of Gossypium lineage ensuing drastic decrease of 2-nt repeats, of which, "AT" motif type dilapidated in cultivated tetraploids of cotton. CONCLUSION: The outcome could be a directive to explore comparative evolutionary footprints of simple non-coding genetic elements i.e., repeat elements, through the evolution of genus-specific characteristics in cotton genomes.

Studying the nonlinear response of incompressible hyperelastic thin circular cylindrical shells with geometric imperfections.

This study presents a comprehensive analysis of hyperelastic thin cylindrical shells exhibiting initial geometrical imperfections. The nonlinear equations of motion are derived using an improved formulation of Donnell's nonlinear shallow-shell theory and Lagrange's equations, incorporating the small strain hypothesis. Mooney-Rivlin constitutive model is employed to capture the hyperelastic behavior of the material. The coupled nonlinear equations of motion are analytically solved using Multiple-Scale method, which effectively accounts for the inherent nonlinearity of the system. To ensure the model's accuracy, the linear model is verified by comparing the results with those obtained through hybrid finite element method. Subsequently, the model with only geometrical nonlinearity is evaluated against other research works existing in the open literature to ensure its reliability and precision. Finally, the results of the model, considering both geometrical and physical nonlinearity, are verified against the results obtained from Abaqus software. The main objective of this research is to provide a detailed understanding of the response of hyperelastic thin cylindrical shells in the presence of initial geometric imperfections. In this order, the impact of three distinct geometric imperfections - axisymmetric, asymmetric, and a combination of driven and companion modes - on the natural frequency is examined. The behavior of each of these geometric imperfections is investigated by varying their respective coefficients. The numerical results indicate that geometric imperfections enhance the natural frequency, and employing different models for imperfections leads to a variation in this trend. In the amplitude response of hyperelastic cylindrical shells, two peaks coexist, reflecting the softening and hardening responses of the system. Distinct initial geometric imperfections influence these two peaks.

Exploring the Impact of Noise and Image Quality on Deep Learning Performance in DXA Images.

BACKGROUND AND OBJECTIVE: Segmentation of the femur in Dual-Energy X-ray (DXA) images poses challenges due to reduced contrast, noise, bone shape variations, and inconsistent X-ray beam penetration. In this study, we investigate the relationship between noise and certain deep learning (DL) techniques for semantic segmentation of the femur to enhance segmentation and bone mineral density (BMD) accuracy by incorporating noise reduction methods into DL models. METHODS: Convolutional neural network (CNN)-based models were employed to segment femurs in DXA images and evaluate the effects of noise reduction filters on segmentation accuracy and their effect on BMD calculation. Various noise reduction techniques were integrated into DL-based models to enhance image quality before training. We assessed the performance of the fully convolutional neural network (FCNN) in comparison to noise reduction algorithms and manual segmentation methods. RESULTS: Our study demonstrated that the FCNN outperformed noise reduction algorithms in enhancing segmentation accuracy and enabling precise calculation of BMD. The FCNN-based segmentation approach achieved a segmentation accuracy of 98.84% and a correlation coefficient of 0.9928 for BMD measurements, indicating its effectiveness in the clinical diagnosis of osteoporosis. CONCLUSIONS: In conclusion, integrating noise reduction techniques into DL-based models significantly improves femur segmentation accuracy in DXA images. The FCNN model, in particular, shows promising results in enhancing BMD calculation and clinical diagnosis of osteoporosis. These findings highlight the potential of DL techniques in addressing segmentation challenges and improving diagnostic accuracy in medical imaging.

Research on prediction of coal water medium separation effect based on multi-models.

To improve the separation efficiency of raw coal and ensure clean use, the accurate calculation of the partition coefficients (PCs) in coal water medium sorting is required. Single models have been used to predict the partition coefficient (PC) for decades, but their accuracy remains constrained. This study proposes a multi-model (MM) calculation method based on the Gompertz model (GM), the Logistic model (LM), the Arctangent model (AM), and the Approximate formula (AFM) to improve the accuracy of the predicted coal water medium sorting PCs. Four groups of coal samples and two specific cases were used to verify the accuracy of the MM calculation method. The PCs of the MM method had a minimal Ef (0.91-8.84), a maximal R2 (0.9648-0.9994), a maximal F-value (199.17-11352.31), and the highest significance of all the models. The MM method was found to be the most suitable of all the models for predicting any coal water medium separation process. Further, when calculating the PC for cleaned coal ash, the separation density of MM is closer to the actual separation density than that of either the GM, LM, AM, or AFM models. The MM method, therefore, produces more accurate results compared to a single model. MM is expected to predict the PC based on the required cleaned coal ash, and then regulate the sorting density to improve the production efficiency.

Numerical investigation of elastic-plastic buckling performance of circular arched cellular steel beam using nonlinear finite element analysis method.

This study presents a numerical investigation of the in-plane elastic-plastic performance, post-buckling mode, and arched web-post shear resistance of a pinned end circular arched cellular steel beam using ABAQUS nonlinear finite element analysis package. The trustworthiness of the finite element analysis results was confirmed by comparing them to the existing experimental investigation results. The main study parameters, such as the effect of a rise-to-span ratio, the radius of curvature, the impact of opening, the types of loading on elastic-plastic performance, and the buckling mode of an arched cellular steel beam, were investigated. Furthermore, the arched web-post finite element model was proposed and the shear resistance of the arched web-post was investigated. Also, the appropriateness of the currently existing different web-post shear resistance analysis approaches was reviewed and evaluated in determining the shear resistance of arched web-posts. The results showed that the web post-structural stiffness of a circular arched cellular steel beam was improved as the rise-to-span ratio increased under the mid-span concentrated load regardless of the rise-to-span ratio. However, under uniformly distributed vertical load, increasing a rise-to-span ratio beyond 0.35 or 140° subtended angles reduces the stiffness of circular arched cellular steel beams. The web post shear resistance analyzing approaches proposed by Panedpojaman et al. and SCI P-100 overestimate and yield unsafe results in determining the web-post shear resistance of arched web post cellular steel of low rise-to-span ratio.

Application of Hashin-Shtrikman bounds homogenization model for frequency analysis of imperfect FG bio-composite plates.

Despite abundant theoretical investigations on the dynamic behavior of functionally graded (FG) structures, the study on frequency analysis of FG bio-composite structures is limited. FG bio-composite materials due to their biocompatibility potentials and good material properties can be applied in biomedical applications, especially dental implants. In this investigation, a natural frequency response of the FG bio-composite plate is analyzed within the framework of the newly developed refined higher-order shear deformation plate theory. Additionally, the imperfection impact on frequency behavior is evaluated while three imperfection distribution patterns are taken into account. The constitutive materials of FG bio-composite plate are Hydroxyapatite and Titanium. The effective material properties of the structure are determined with the help of the upper Hashin-Shtrikman bounds homogenization model. In continuation, to solve the derived governing equations of imperfect FG bio-composite plate, Galerkin's analytical method is employed. Also, the precision of the used theory is validated, the obtained outcomes are compared and an acceptable matching is found. Later, the sensitivity of different considerable variables is comprehensively assessed and discussed.

Impact of imperfection in medical imaging data on deep learning-based segmentation performance: An experimental study using synthesized data.

BACKGROUND: Clinical data used to train deep learning models are often not clean data. They can contain imperfections in both the imaging data and the corresponding segmentations. PURPOSE: This study investigates the influence of data imperfections on the performance of deep learning models for parotid gland segmentation. This was done in a controlled manner by using synthesized data. The insights this study provides may be used to make deep learning models better and more reliable. METHODS: The data were synthesized by using the clinical segmentations, creating a pseudo ground-truth in the process. Three kinds of imperfections were simulated: incorrect segmentations, low image contrast, and artifacts in the imaging data. The severity of each imperfection was varied in five levels. Models resulting from training sets from each of the five levels were cross-evaluated with test sets from each of the five levels. RESULTS: Using synthesized data led to almost perfect parotid gland segmentation when no error was added. Lowering the quality of the parotid gland segmentations used for training substantially lowered the model performance. Additionally, lowering the image quality of the training data by decreasing the contrast or introducing artifacts made the resulting models more robust to data containing those respective kinds of data imperfection. CONCLUSION: This study demonstrated the importance of good-quality segmentations for deep learning training and it shows that using low-quality imaging data for training can enhance the robustness of the resulting models.

Physical appearance perfectionism in blepharoplasty patients: A prospective observational study.

BACKGROUND: Evaluation of physical appearance perfectionism (PAP) in individuals seeking blepharoplasty would be meaningful. This study aimed to explore the relationship of demographic and psychological variables with PAP in blepharoplasty patients and further investigate the impact of blepharoplasty on PAP in these patients. METHODS: This prospective observational study included 153 patients undergoing blepharoplasty between October 2017 and June 2019. Demographic and psychological variables, and PAP, were collected preoperatively. Postoperative satisfaction with eye appearance and PAP was collected with a 6-month follow-up. RESULTS: Partial correlations analyses revealed that hope for perfection was positively associated with self-esteem (r = 0.246; P < 0.01) in 153 blepharoplasty patients. Worry about imperfection was positively related to facial appearance concern (r = 0.703; P < 0.001) and negatively related to satisfaction with eye appearance (r = -0.242; P < 0.01) and self-esteem (r = -0.533; P < 0.001). After blepharoplasty, the mean± standard deviation of satisfaction with eye appearance increased (preoperatively vs. postoperatively: 5.1 ± 2.2 vs. 7.4 ± 2.2; P < 0.001), and worry about imperfection decreased (17.0 ± 4.2 vs. 15.9 ± 4.6; P < 0.001). Whereas hope for perfection remained unchanged (23.9 ± 3.9 vs. 23.6 ± 3.9; P > 0.05). CONCLUSIONS: Appearance perfectionism was related to psychological variables rather than demographic variables in blepharoplasty patients. Preoperative evaluation of appearance perfectionism could be helpful for oculoplastic surgeons to screen for perfectionistic patients. Although some improvement in perfectionism has been observed after blepharoplasty, long-term follow-up is needed in the future.

CPMG pulse sequence for relaxation dispersion that cancels artifacts independently of spin states.

The relaxation dispersion (rd) of nuclear magnetic resonance provides thermodynamic and kinetic information regarding molecules for which the conformations are exchanging in equilibrium. Experiments have often been implemented with Carr-Purcell-Meiboom-Gill (CPMG) pulse sequences for heteronuclear S-spin in SI and SI3 spin systems. One of the most common CPMG sequences contains a sequence called a P-element in the middle to average the different relaxation rates of anti-phase and in-phase coherences; however, its drawback is that the artifacts that can be compensated for are only those in one of the two S-spin doublet magnetization components, for example, SIα or SIß in an SI spin system. Thus, when the CPMG sequence is followed by a normal heteronuclear single-quantum correlation (HSQC) sequence, the detected signal will also include the other component with accumulated artifacts. To overcome this issue, we developed a new pulse sequence (AFTAC) that can suppress artifacts in both the magnetization components. Its effectiveness was demonstrated by simulations and actual measurements targeting the methyl groups of dimethylsulfoxide and N, N-dimethyltrichloroacetamide. The results demonstrated that the AFTAC sequence sufficiently suppressed the artifacts, despite being followed by an HSQC sequence that detects both components. AFTAC is particularly suitable for the rd measurements of small molecules, which are usually not deuterated and are not subject to transverse relaxation optimized spectroscopy (TROSY) sequences. AFTAC does not require 1H continuous wave irradiation for I-spin decoupling, which is necessary for certain CPMG methods that maintain S-spin in-phase coherence during the CPMG period (Tcpmg). Therefore, AFTAC places less burden on the probe, even with a long Tcpmg. Furthermore, the AFTAC method achieves a similar artifact suppression quality not only in SI but also in SI2 and SI3 spin systems.

Lightweight Design of Variable-Stiffness Cylinders with Reduced Imperfection Sensitivity Enabled by Continuous Tow Shearing and Machine Learning.

The present study investigates how to apply continuous tow shearing (CTS) in a manufacturable design parameterization to obtain reduced imperfection sensitivity in lightweight, cylindrical shell designs. The asymptotic nonlinear method developed by Koiter is applied to predict the post-buckled stiffness, whose index is constrained to be positive in the optimal design, together with a minimum design load. The performance of three machine learning methods, namely, Support Vector Machine, Kriging, and Random Forest, are compared as drivers to the optimization towards lightweight designs. The new methodology consists of contributions in the areas of problem modeling, the selection of machine learning strategies, and an optimization formulation that results in optimal designs around the compromise frontier between mass and stiffness. The proposed ML-based framework proved to be able to solve the inverse problem for which a target design load is given as input, returning as output lightweight designs with reduced imperfection sensitivity. The results obtained are compatible with the existing literature where hoop-oriented reinforcements were added to obtain reduced imperfection sensitivity in composite cylinders.

Numerical Simulation of Local Buckling of Submarine Pipelines under Combined Loading Conditions.

Submarine pipelines are prone to developing flaws, such as ellipticity and depression during the manufacture, burying, and use processes. The local buckling characteristics of submarine pipelines with initial imperfection must be studied since the initial imperfection have an impact on local pipeline buckling. In this study, the local buckling of submarine pipelines with varying depression depths and ellipticity is simulated using the finite element program ABAQUS, and defect sensitivity of submarine pipelines with varying shape ellipticity, varying depression depths, and varying pipe radius-thickness ratios is examined. Meanwhile, research is being conducted on the combined load buckling of a submarine pipeline with initial imperfection caused by bending, axial force, and external hydrostatic pressure. The results indicated that the critical external pressure of the pipeline is sensitive to the imperfection, although the buckling propagation pressure is not. The buckling morphology is influenced by the shape and size of the imperfection. Additionally, the ability to withstand external hydrostatic pressure of the pipeline reduces after it has been bent.

Quantification of Interdependent Dynamics during Laser Additive Manufacturing Using X-Ray Imaging Informed Multi-Physics and Multiphase Simulation.

Laser powder bed fusion (LPBF) can produce high-value metallic components for many industries; however, its adoption for safety-critical applications is hampered by the presence of imperfections. The interdependency between imperfections and processing parameters remains unclear. Here, the evolution of porosity and humps during LPBF using X-ray and electron imaging, and a high-fidelity multiphase process simulation, is quantified. The pore and keyhole formation mechanisms are driven by the mixing of high temperatures and high metal vapor concentrations in the keyhole is revealed. The irregular pores are formed via keyhole collapse, pore coalescence, and then pore entrapment by the solidification front. The mixing of the fast-moving vapor plume and molten pool induces a Kelvin-Helmholtz instability at the melt track surface, forming humps. X-ray imaging and a high-fidelity model are used to quantify the pore evolution kinetics, pore size distribution, waviness, surface roughness, and melt volume under single layer conditions. This work provides insights on key criteria that govern the formation of imperfections in LPBF and suggest ways to improve process reliability.

Sustainable yarn production using leftover fabric from apparel industries.

A huge amount of waste was generated from the apparel industries. This study aims to develop the process of producing recycled yarn from apparel waste. The apparel leftover fabric was converted to fiber, and the fiber was mixed with virgin cotton in different ratios to produce sustainable 6/1 Ne rotor yarn. The produced yarn qualities viz. count strength product (CSP), elongation percentage, total quality index (TQI) and tenacity were decreased linearly, and opposite scenario observed for thick and thin places, neps, imperfection index (IPI) and hairiness (H) attributes with increasing the amount of waste addition with virgin cotton. The leftover fabric (LOF) can be utilized to develop a sustainable yarn and to zero waste management.