Search

The softening of aluminum–lithium alloy welded joints generally leads to a reduction in mechanical properties. In this study, a piece of 2A97-T3 aluminum–lithium alloy with a thickness of 2.8 mm was selected as the test material, and the tool and process used for wire-filled stationary shoulder friction stir welding (SSFSW) were developed. By increasing the bearing area of the softening zone, an equal-strength T-joint was manufactured. Good weld formation was obtained when the rotation speed was set to 2000 rpm and the welding speed ranged from 100 to 120 mm/min. The thickness of the softening zone was controlled by adjusting the reserved gap between the shoulder and the workpiece. The softening mechanism of the weld joint was revealed. The softening was attributed to the coarsening of the main precipitated phases (T1 and θ′ phases) in the heat-affected zone (HAZ) and the dissolution of precipitated phases in the thermo-mechanically affected zone (TMAZ). Grain refinement in the nugget zone (NZ) led to a certain fine-grained strengthening effect, although the precipitated phase was almost completely dissolved. Due to the thermal effect of second-pass welding, the hardness value of the NZ and HAZ in the center of the skin further decreased, and the minimum hardness was approximately 70% that of the base material. Tensile testing results indicated that the softening effect was largely offset by the increased bearing area of the softening zone, resulting in the successful welding of high-strength Al-Li alloy T-joints with equal strength. The strength coefficient was found to be 0.977. [ABSTRACT FROM AUTHOR]

Recent advances in single-cell technologies enable the rapid growth of multi-omics data. Cell type annotation is one common task in analyzing single-cell data. It is a challenge that some cell types in the testing set are not present in the training set (i.e. unknown cell types). Most scATAC-seq cell type annotation methods generally assign each cell in the testing set to one known type in the training set but neglect unknown cell types. Here, we present OVAAnno, an automatic cell types annotation method which utilizes open-set domain adaptation to detect unknown cell types in scATAC-seq data. Comprehensive experiments show that OVAAnno successfully identifies known and unknown cell types. Further experiments demonstrate that OVAAnno also performs well on scRNA-seq data. Our codes are available online at https://github.com/lisaber/OVAAnno/tree/master. [ABSTRACT FROM AUTHOR]

In this paper, Gleeble-3500 thermal simulator was employed to perform single-pass isothermal compression tests of a spray formed 2050 Al-Cu-Li alloy at a temperature range of 350 to 470 ℃ and a strain rate range of 0.01 to 5 s−1. The flow stress curves of the alloy were captured and the flow data were made into 3D hot processing map subsequently as a guidance for industrial production. At the same time, EBSD technique was applied to characterize the evolution of hot deformation microstructure of the alloy. The results show that the flow stress of the alloy increases with the decreasing deformation temperature and rising strain rate. 3D hot processing map shows that the dissipation power of the alloy has two peaks, and the optimal hot working interval is from to 450-470 ℃, 0.01 - 0.1 s−1. The microstructure analysis of EBSD shows that the original grains of the alloy are significantly elongated after hot deformation, and a large number of recrystallized grains are found in the intergrain and grain boundary of the alloy at 470 ℃/0.01 s−1, which indicates that the alloy has good hot working performance under such deformation conditions.

This paper investigates the effect of retrogression time on the fatigue crack growth of a modified AA7475 aluminum alloy. Tests including tensile strength, fracture toughness, and fatigue limits were performed to understand the changes in properties with different retrogression procedures at 180 °C. The microstructure was characterized using scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The findings indicated that as the retrogression time increased, the yield strength decreased from 508 MPa to 461 MPa, whereas the fracture toughness increased from 48 MPa√m to 63.5 MPa√m. The highest fracture toughness of 63.5 MPa√m was seen after 5 h of retrogression. The measured diameter of η' precipitates increased from 6.13 nm at the retrogression 1 h condition to 6.50 nm at the retrogression 5 h condition. Prolonged retrogression also increased the chance of crack initiation, with slower crack growth rate in the long transverse direction compared to the longitudinal direction. An empirical relationship was established between fracture toughness and the volume fraction of age-hardening precipitates, with increasing number density of precipitates seen with increasing retrogression time. [ABSTRACT FROM AUTHOR]

Recent advances in spatial transcriptomics have enabled measurements of gene expression at cell/spot resolution meanwhile retaining both the spatial information and the histology images of the tissues. Accurately identifying the spatial domains of spots is a vital step for various downstream tasks in spatial transcriptomics analysis. To remove noises in gene expression, several methods have been developed to combine histopathological images for data analysis of spatial transcriptomics. However, these methods either use the image only for the spatial relations for spots, or individually learn the embeddings of the gene expression and image without fully coupling the information. Here, we propose a novel method ConGI to accurately exploit spatial domains by adapting gene expression with histopathological images through contrastive learning. Specifically, we designed three contrastive loss functions within and between two modalities (the gene expression and image data) to learn the common representations. The learned representations are then used to cluster the spatial domains on both tumor and normal spatial transcriptomics datasets. ConGI was shown to outperform existing methods for the spatial domain identification. In addition, the learned representations have also been shown powerful for various downstream tasks, including trajectory inference, clustering, and visualization. [ABSTRACT FROM AUTHOR]

Aluminum extruded profile is widely applied in aircraft frame parts whose forming quality is directly related to the assembly accuracy, aerodynamic shape and the service life of aircraft. However, it normally faces difficulties of large springback and low forming limit while forming at room temperature by stretch bending method. Here, electricity was applied in stretch bending process to heat aluminum profile aiming at improving the formability and reducing the springback by using electrically assisted stretch bending machine which is consisted of electrical power with the capability of 10000A and 40000kN stretching force for each hydraulic cylinder. In this research, a set of uniaxial tension tests was performed at different temperature to determine the relationship between the deformation behavior of aluminum profile and temperature. Then, a series of stretch bending tests were conducted to investigate the springback law of the aluminum profile. The corresponding stretch bending parts were obtained under different conditions. The experiment results show that, the plasticity of aluminum profile can be improved when the temperature rises, and the springback of aluminum profile can be reduced even more due to the electrical heating effect compared to the cold stretch bending process. Therefore, the forming accuracy of aluminum profile can be improved in electrically assisted stretch bending process.

The rapid development of spatial transcriptomics allows the measurement of RNA abundance at a high spatial resolution, making it possible to simultaneously profile gene expression, spatial locations of cells or spots, and the corresponding hematoxylin and eosin-stained histology images. It turns promising to predict gene expression from histology images that are relatively easy and cheap to obtain. For this purpose, several methods are devised, but they have not fully captured the internal relations of the 2D vision features or spatial dependency between spots. Here, we developed Hist2ST, a deep learning-based model to predict RNA-seq expression from histology images. Around each sequenced spot, the corresponding histology image is cropped into an image patch and fed into a convolutional module to extract 2D vision features. Meanwhile, the spatial relations with the whole image and neighbored patches are captured through Transformer and graph neural network modules, respectively. These learned features are then used to predict the gene expression by following the zero-inflated negative binomial distribution. To alleviate the impact by the small spatial transcriptomics data, a self-distillation mechanism is employed for efficient learning of the model. By comprehensive tests on cancer and normal datasets, Hist2ST was shown to outperform existing methods in terms of both gene expression prediction and spatial region identification. Further pathway analyses indicated that our model could reserve biological information. Thus, Hist2ST enables generating spatial transcriptomics data from histology images for elucidating molecular signatures of tissues. [ABSTRACT FROM AUTHOR]

Clustering analysis is widely used in single-cell ribonucleic acid (RNA)-sequencing (scRNA-seq) data to discover cell heterogeneity and cell states. While many clustering methods have been developed for scRNA-seq analysis, most of these methods require to provide the number of clusters. However, it is not easy to know the exact number of cell types in advance, and experienced determination is not always reliable. Here, we have developed ADClust, an automatic deep embedding clustering method for scRNA-seq data, which can accurately cluster cells without requiring a predefined number of clusters. Specifically, ADClust first obtains low-dimensional representation through pre-trained autoencoder and uses the representations to cluster cells into initial micro-clusters. The clusters are then compared in between by a statistical test, and similar micro-clusters are merged into larger clusters. According to the clustering, cell representations are updated so that each cell will be pulled toward centers of its assigned cluster and similar clusters, while cells are separated to keep distances between clusters. This is accomplished through jointly optimizing the carefully designed clustering and autoencoder loss functions. This merging process continues until convergence. ADClust was tested on 11 real scRNA-seq datasets and was shown to outperform existing methods in terms of both clustering performance and the accuracy on the number of the determined clusters. More importantly, our model provides high speed and scalability for large datasets. [ABSTRACT FROM AUTHOR]

The contact process of stator and slider described by the Coulomb friction model is basically in a pure sliding friction state, and a mechanical model based on the Dahl friction theory was proposed to describe the contact process between stator and slider of V-shape linear ultrasonic motor. With consideration for the tangential compliance of stator and slider, the dynamic contact and friction processes of stator and slider were addressed in stages. The simulation results show that the ratio of the friction positive work decreases with the increase of the preload, and the vibration amplitude of the stator increases the proportion of positive work of the friction force. Improving the contact stiffness of the stator and slider is conducive to improving the output performance of the ultrasonic motor. The asymmetry of the left and right performance of the V-shaped vibrator will cause a difference in the left and right running speeds of the ultrasonic motor. The improved Dahl friction-driving model makes up for the discontinuity of tangential contact force calculated by the Coulomb friction model. This study demonstrates that the friction-driving model based on the Dahl theory is reliable and reasonable for linear ultrasonic motors according to the experimental results. [ABSTRACT FROM AUTHOR]

In this study, a series of optically transparent metallized acrylics containing Gd and Pb were synthesized by the bulk polymerization of Gd(MAA)3, Pb(MAA)2 and AM according to different polymerization procedures. The variation of their optic transmittance and mechanical performance with Gd contents was investigated. Then, quasi-static uniaxial tensile tests under different strain rates and temperatures were performed to study the influence of strain rate and temperature on the mechanical properties of radiation-shielding metallized acrylic containing both Gd and Pb. The tensile responses of this material distinctly exhibit nonlinear characteristics and strongly depend on both temperature and strain rate. Based on the experimental results, a modified Zhu–Wang–Tang (ZWT) constitutive model, in which the standard elastic component was replaced by the Mooney–Rivlin hyperelastic model, was implemented to characterize the observed both hyperelastic and viscoelastic behaviors. The constitutive parameters were expressed as functions of temperature and determined by experimental data. The model fitting results indicate that the selected constitutive model can accurately describe the nonlinear tensile stress–strain responses of metallized acrylic containing Gd and Pb. Furthermore, the great difference in constitutive parameters implies that the viscoelastic behavior of the as-prepared metallized acrylic affects the response to quasi-static tensile loading the most. [ABSTRACT FROM AUTHOR]

Single-cell RNA sequencing (scRNA-seq) techniques provide high-resolution data on cellular heterogeneity in diverse tissues, and a critical step for the data analysis is cell type identification. Traditional methods usually cluster the cells and manually identify cell clusters through marker genes, which is time-consuming and subjective. With the launch of several large-scale single-cell projects, millions of sequenced cells have been annotated and it is promising to transfer labels from the annotated datasets to newly generated datasets. One powerful way for the transferring is to learn cell relations through the graph neural network (GNN), but traditional GNNs are difficult to process millions of cells due to the expensive costs of the message-passing procedure at each training epoch. Here, we have developed a robust and scalable GNN-based method for accurate single-cell classification (GraphCS), where the graph is constructed to connect similar cells within and between labelled and unlabeled scRNA-seq datasets for propagation of shared information. To overcome the slow information propagation of GNN at each training epoch, the diffused information is pre-calculated via the approximate Generalized PageRank algorithm, enabling sublinear complexity over cell numbers. Compared with existing methods, GraphCS demonstrates better performance on simulated, cross-platform, cross-species and cross-omics scRNA-seq datasets. More importantly, our model provides a high speed and scalability on large datasets, and can achieve superior performance for 1 million cells within 50 min. [ABSTRACT FROM AUTHOR]

This paper presents a study on the creep-ageing behaviour of a peak-aged aluminium alloy 7B04 under different tensile loads at 115oC and subsequently modelling it for creep-age forming (CAF) applications. Mechanical properties and microstructural evolutions of creep-aged specimens were investigated. The material was modelled using a set of unified constitutive equations, which not only captures the material's creep deformation but also takes into account yield strength contributions from solid solution hardening, age hardening and dislocation hardening during creep-ageing. A possible application of the present work is demonstrated by implementing the determined material model into a commercial finite element analysis solver via a user-defined subroutine for springback prediction of creep-age formed plates. A good agreement is observed between the simulated springback values and experimental results. This material model now enables further investigations of 7B04 under various CAF scenarios to be conducted inexpensively via computational modelling.

Creep-ageing behaviour of aluminium alloy 7B04-T651 at 115 °C under a range of tensile stress levels has been experimentally investigated and numerically modelled for creep-age forming (CAF) applications. Creep strain, yield strength evolution and precipitate growth of creep-aged specimens were investigated. The alloy was modelled using a set of unified constitutive equations, which captures its creep deformation and takes into account yield strength contributions from three creep-age hardening mechanisms. Applications of the present work are demonstrated by implementing the determined material model into a commercial finite element analysis solver to analyse CAF operations carried out in a novel flexible CAF tool. Stress relaxation, yield strength, precipitate size and springback were predicted for the creep-age formed plates. The predicted springback were further quantified and compared with experimental measurements and a good agreement of 2.5% deviation was achieved. This material model now enables further investigations of 7B04 under various CAF scenarios to be conducted inexpensively via computational modelling.

In single cell analyses, cell types are conventionally identified based on expressions of known marker genes, whose identifications are time-consuming and irreproducible. To solve this issue, many supervised approaches have been developed to identify cell types based on the rapid accumulation of public datasets. However, these approaches are sensitive to batch effects or biological variations since the data distributions are different in cross-platforms or species predictions. In this study, we developed scAdapt, a virtual adversarial domain adaptation network, to transfer cell labels between datasets with batch effects. scAdapt used both the labeled source and unlabeled target data to train an enhanced classifier and aligned the labeled source centroids and pseudo-labeled target centroids to generate a joint embedding. The scAdapt was demonstrated to outperform existing methods for classification in simulated, cross-platforms, cross-species, spatial transcriptomic and COVID-19 immune datasets. Further quantitative evaluations and visualizations for the aligned embeddings confirm the superiority in cell mixing and the ability to preserve discriminative cluster structure present in the original datasets. [ABSTRACT FROM AUTHOR]

Electromagnetic wave loss caused by rotating polarization of chiral structures has long been considered as a disadvantage in transmission, detection, etc., however, it can be turned to benefit in microwave absorbing materials to enhance absorption. Herein, inspired by the chiral structure of jeweled beetles, gyrotropic metamaterials (MMs) are designed and prepared, and experimentally demonstrate the enhanced microwave absorption by rotating polarization; the bandwidth of absorptivity greater than 80% (reflection loss, RL ≤ −7 dB) covers the entire 4–18 GHz frequency range under an ultralow surface density of 1.45 kg m−2; the mechanism analyzed and verified by simulation models demonstrates this a universal method. A new paradigm of MMs fabricated by high entropy alloy (HEA) is offered in this work, which increases the freedom of MM design and promotes the integration of material science and physics. Multifarious wave adaptability and multifunctionality achieved by integrated HEA unit cell and foamed polyurethane realize ultrasonic and water wave absorption, as well as impact and corrosion resistance. These designs hold great promise for facilitating functionally integrated materials for stealth technology, antiterrorist, encryption, sensing, and photon detection. [ABSTRACT FROM AUTHOR]

Magnetic microwave absorbing materials (MMAMs) possess many desirable properties, including thin thickness and wide attenuation band, and thus are widely applied in many fields. However, the mechanisms of variation in performance of MMAMs at dynamic temperature are difficult to characterize and poorly understood. In this work, we combine experiments and fluctuation-induced tunneling (FIT) simulations to delineate the effects of temperature on the microwave absorption properties of MMAMs. We find that the increase in temperature weakens the complex permeability of the carbonyl iron particles (CIPs)/epoxy coating, while also strengthening the complex permittivity. FIT simulations explain the experimental observations by showing that the tunneling current between two CIPs with a gap of a few nanometers increases with increasing temperature. Furthermore, crescendo conductance loss and decrescendo magnetic loss form an electric-magnetic complementary effect, so that the coating exhibits superior microwave absorption performance (maximum effective bandwidth 8 GHz) in the temperature range of 293 K–573 K, a result with implications for the broad understanding and design of MMAMs applied at high temperatures. [ABSTRACT FROM AUTHOR]

Unified creep-ageing constitutive equations for aluminium alloy 7050 (AA7050) determined from experimental data were implemented into the finite element (FE) code, ABAQUS, through the user-defined subroutine, CREEP, for creep age forming (CAF) process modelling. The outer contour and yield strength of a saddle-shaped component were predicted, and the accuracy of FE prediction was analysed with experimental results. The research results show that, stress relaxation and creep deformation occur mainly in the early stage of the CAF process. A close agreement has been achieved between the simulation results and measurement data for the formed shape. More than 90% of the area has a surface gap less than 0.5 mm, and the relative error of the yield strength is between -1.18% and +7.22%, which prove the validity of the numerical computation. [ABSTRACT FROM AUTHOR]

Flight feather shafts are outstanding bioinspiration templates due to their unique light weight and their stiff and strong characteristics. As a thin wall of a natural composite beam, the keratinous cortex has evolved anisotropic features to support flight. Here, the anisotropic keratin composition, tensile response, dynamic properties of the cortex, and fracture behaviors of the shafts are clarified. The analysis of Fourier transform infrared (FTIR) spectra indicates that the protein composition of calamus cortex is almost homogeneous. In the middle and distal shafts (rachis), the content of the hydrogen bonds (HBs) and side-chain is the highest within the dorsal cortex and is consistently lower within the lateral wall. The tensile responses, including the properties and dominant damage pattern, are correlated with keratin composition and fiber orientation in the cortex. As for dynamic properties, the storage modulus and damping of the cortex are also anisotropic, corresponding to variation in protein composition and fibrous structure. The fracture behaviors of bent shafts include matrix breakage, fiber dissociation and fiber rupture on compressive dorsal cortex. To clarify, 'real-time' damage behaviors, and an integrated analysis between AE signals and fracture morphologies, are performed, indicating that calamus failure results from a straight buckling crack and final fiber rupture. Moreover, in the dorsal and lateral walls of rachis, the matrix breakage initially occurs, and then the propagation of the crack is restrained by 'ligament-like' fiber bundles and cross fiber, respectively. Subsequently, the further matrix breakage, interface dissociation and induced fiber rupture in the dorsal cortex result in the final failure. [ABSTRACT FROM AUTHOR]