Recovery Of Matrix Research Articles

Label recovery and label correlation co-learning for multi-view multi-label classification with incomplete labels.

Multi-view multi-label learning (MVML) is an important paradigm in machine learning, where each instance is represented by several heterogeneous views and associated with a set of class labels. However, label incompleteness and the ignorance of both the relationships among views and the correlations among labels will cause performance degradation in MVML algorithms. Accordingly, a novel method, label recovery and label correlation co-learning for Multi-View Multi-Label classification with incoMplete Labels (MV2ML), is proposed in this paper. First, a label correlation-guided binary classifier kernel-based is constructed for each label. Then, we adopt the multi-kernel fusion method to effectively fuse the multi-view data by utilizing the individual and complementary information among multiple views and distinguishing the contribution difference of each view. Finally, we propose a collaborative learning strategy that considers the exploitation of asymmetric label correlations, the fusion of multi-view data, the recovery of incomplete label matrix and the construction of the classification model simultaneously. In such a way, the recovery of incomplete label matrix and the learning of label correlations interact and boost each other to guide the training of classifiers. Extensive experimental results demonstrate that MV2ML achieves highly competitive classification performance against state-of-the-art approaches on various real-world multi-view multi-label datasets in terms of six evaluation criteria.

Optimal recovery of precision matrix for Mahalanobis distance from high-dimensional noisy observations in manifold learning

Abstract Motivated by establishing theoretical foundations for various manifold learning algorithms, we study the problem of Mahalanobis distance (MD) and the associated precision matrix estimation from high-dimensional noisy data. By relying on recent transformative results in covariance matrix estimation, we demonstrate the sensitivity of MD and the associated precision matrix to measurement noise, determining the exact asymptotic signal-to-noise ratio at which MD fails, and quantifying its performance otherwise. In addition, for an appropriate loss function, we propose an asymptotically optimal shrinker, which is shown to be beneficial over the classical implementation of the MD, both analytically and in simulations. The result is extended to the manifold setup, where the nonlinear interaction between curvature and high-dimensional noise is taken care of. The developed solution is applied to study a multi-scale reduction problem in the dynamical system analysis.

Effect of pulse current treatment on interface structure and mechanical behavior of TA1/304 clad plates

To ensure the as-rolled clad plates have good machinability, annealing treatment is an effective method to control the microstructure and mechanical properties of titanium/steel clad plates after rolling. For the traditional annealing, the interface structure is seriously deteriorated after 2–3 h annealing treatment. Here, we report a new method of pulse current assisted treatment in short time after rolling. The influence of current density and treatment time was systematically studied. The degree of recovery and recrystallization of matrix as well as the thickness of the interface reaction layer increases with the increase of current density and treatment time. Finally, after 25.30 A/mm2 and 1 min pulse current treatment, the titanium/steel clad plates has good comprehensive property with an elongation of 48% and tensile strength of 737 MPa. It can be attributed that the dislocation density of the matrix decreases rapidly in a short time under the action of pulse current and the intermetallic compounds (IMCs) are rarely formed. This discovery can greatly improve the efficiency of clad plate treatment after rolling and reduce production costs.

Investigation into the thermal stability of a novel hot-work die steel 5CrNiMoVNb

Abstract A novel hot-work die steel 5CrNiMoVNb is developed by optimizing the alloy composition of 5CrNiMoV steel. Thermal stability tests were carried out to compare the hardness evolution of the two steel types. The hardness reduction of 5CrNiMoVNb at 600 and 650°C was only 4.3HRC and 9.6HRC, while that of 5CrNiMoV steel at the same condition was as large as 6.5HRC and 17.5HRC, respectively, which suggests that the thermal stability of the 5CrNiMoVNb steel is more excellent. The thermal stability mechanism of 5CrNiMoVNb was studied based on microstructure analyses and thermodynamic calculations. This suggests that high tempering temperatures cause the coarsening of some carbides and suppress the recovery and recrystallization of the martensite matrix, which is the main reason for the slight decrease in the thermal stability. For the adding of the medium and strong carbide-forming elements, the carbides in 5CrNiMoVNb steel are mainly MC and M23C6 with low coarsening rate coefficient, and the content of these two carbides is almost constant below 670°C. The fine MC and M23C6 carbides showed strong pinning and dragging effects on the dislocations and suppressed martensite recovery and recrystallization. Therefore, the novel hot-work die steel showed excellent tempering softening resistance and thermal stability than 5CrNiMoV steel.

A novel approach for in-situ preparation of copper/graphene composite with high hardness and high electrical conductivity

A bulk Cu/graphene-Al2O3 composite with high electrical conductivity (96.95% IACS) and high hardness (87.21 HV) was successfully fabricated through vacuum hot pressing sintering by introducing aluminum isopropoxide as the carbon source. The in-situ formed graphene with high-quality distributes along the grain boundary, the nano sized Al2O3 particles generated near the interface. Both graphene and Al2O3 nano particles greatly refine the microstructure of the Cu/graphene composite and strengthen the matrix. The cold rolled Cu/graphene-Al2O3 sheet presents high electrical conductivity and high strength. The coexistence of graphene and Al2O3 nano particles blocks the movement of grain boundaries, delays the recovery and recrystallization process of the Cu matrix and makes the composite possess excellent softening resistance. The work provides a new idea for large-scale preparation of high-performance Cu/graphene composites.

Color-Dense Illumination Adjustment Network for Removing Haze and Smoke from Fire Scenario Images.

The atmospheric particles and aerosols from burning usually cause visual artifacts in single images captured from fire scenarios. Most existing haze removal methods exploit the atmospheric scattering model (ASM) for visual enhancement, which inevitably leads to inaccurate estimation of the atmosphere light and transmission matrix of the smoky and hazy inputs. To solve these problems, we present a novel color-dense illumination adjustment network (CIANet) for joint recovery of transmission matrix, illumination intensity, and the dominant color of aerosols from a single image. Meanwhile, to improve the visual effects of the recovered images, the proposed CIANet jointly optimizes the transmission map, atmospheric optical value, the color of aerosol, and a preliminary recovered scene. Furthermore, we designed a reformulated ASM, called the aerosol scattering model (ESM), to smooth out the enhancement results while keeping the visual effects and the semantic information of different objects. Experimental results on both the proposed RFSIE and NTIRE’20 demonstrate our superior performance favorably against state-of-the-art dehazing methods regarding PSNR, SSIM and subjective visual quality. Furthermore, when concatenating CIANet with Faster R-CNN, we witness an improvement of the objection performance with a large margin.

Reducing the Effects of Motion Artifacts in fMRI: A Structured Matrix Completion Approach.

Functional MRI (fMRI) is widely used to study the functional organization of normal and pathological brains. However, the fMRI signal may be contaminated by subject motion artifacts that are only partially mitigated by motion correction strategies. These artifacts lead to distance-dependent biases in the inferred signal correlations. To mitigate these spurious effects, motion-corrupted volumes are censored from fMRI time series. Censoring can result in discontinuities in the fMRI signal, which may lead to substantial alterations in functional connectivity analysis. We propose a new approach to recover the missing entries from censoring based on structured low rank matrix completion. We formulated the artifact-reduction problem as the recovery of a super-resolved matrix from unprocessed fMRI measurements. We enforced a low rank prior on a large structured matrix, formed from the samples of the time series, to recover the missing entries. The recovered time series, in addition to being motion compensated, are also slice-time corrected at a fine temporal resolution. To achieve a fast and memory-efficient solution for our proposed optimization problem, we employed a variable splitting strategy. We validated the algorithm with simulations, data acquired under different motion conditions, and datasets from the ABCD study. Functional connectivity analysis showed that the proposed reconstruction resulted in connectivity matrices with lower errors in pair-wise correlation than non-censored and censored time series based on a standard processing pipeline. In addition, seed-based correlation analyses showed improved delineation of the default mode network. These demonstrate that the method can effectively reduce the adverse effects of motion in fMRI analysis.

Robust Reweighted ℓ2,1-Norm Based Approach for DOA Estimation in MIMO Radar Under Array Sensor Failures

To alleviate the direction of arrival (DOA) estimation performance degradation under array sensor failures in multiple input multiple output (MIMO) radar, joint DOA estimation and missing data recovery is dealt with by a reweighted <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{2,1}$ </tex-math></inline-formula> -norm framework. Firstly, we employ a technique based on singular value decomposition (SVD) to reduce the dimension of the array data matrix and the sensitivity to observation noise. Then, a novel reweighted <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{2,1}$ </tex-math></inline-formula> -norm minimization framework is introduced to improve the robustness against model mismatch caused by sensor failures, which incorporates the recovery of the complete array data matrix into the sparse reconstruction and constructs the weight matrix for reweighting the mixed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{2,1}$ </tex-math></inline-formula> -norm minimization. Thereafter, the alternative direction method of multipliers (ADMM) under the augmented Lagrange multiplier (ALM) framework is applied to solve this multivariable optimization problem with multiple constraints, in which the sparse solution is iteratively obtained along with the recovery of the complete array data matrix. Finally, the estimation of DOAs can be achieved by finding the non-zero rows in the reconstructed sparse matrix. Simulations demonstrate that the proposed method has better estimation performance than the existing methods for handling array sensor failures and exhibits robustness in the face of a wide range of faulty sensor number.

Shadow tomography based on informationally complete positive operator-valued measure

Recently introduced shadow tomography protocols use ``classical shadows'' of quantum states to predict many target functions of an unknown quantum state. Unlike full quantum state tomography, shadow tomography does not insist on accurate recovery of the density matrix for high rank mixed states. Yet, such a protocol makes multiple accurate predictions with high confidence, based on a moderate number of quantum measurements. One particular influential algorithm, proposed by Huang et al. [Huang, Kueng, and Preskill, Nat. Phys. 16, 1050 (2020)], requires additional circuits for performing certain random unitary transformations. In this paper, we avoid these transformations but employ an arbitrary informationally complete positive operator-valued measure and show that such a procedure can compute $k$-bit correlation functions for quantum states reliably. We also show that, for this application, we do not need the median of means procedure of Huang et al. Finally, we discuss the contrast between the computation of correlation functions and fidelity of reconstruction of low rank density matrices.

Characterizing Graft-Vs-Host Disease of the Bone Marrow Post Allogeneic Hematopoietic Cell Transplantation in Mice

Graft-vs-Host Disease (GVHD) is a common complication post allogeneic hematopoietic cell transplantation (allo-HCT). In contrast to classical target organs alloreactivity directed against hematopoietic and non-hematopoietic bone marrow (BM) structures has been poorly characterized, even though hematopoietic dysfunction following allo-HCT is frequently observed.Here, we studied in MHC-matched, minor antigen mismatched HCT models (C3H-SW > C57BL/6 (B6)) the effects of lethal total body irradiation and HCT of HSC (cKIT+Sca1+Lin-) alone or in combination with Tc / Tc subsets (CD4+, CD8+ or total (CD4+CD8) T cells (Tc)) on the hematopoietic compartment (HC) and non-hematopoietic compartment (NHC) of the BM. Up to 8 weeks (w) post-HCT bones and marrow were analyzed by flow cytometry and 3D-confocal microscopy imaging.Post allo-HCT there was a transient weight loss and decrease of total BM cell counts in all experimental groups, but no signs of overt GVHD. At 2w mice given HSC had significantly higher absolute BM counts compared with HSC+Tc/Tc subset recipients. Most pronouncedly affected were B-cells that recovered promptly in HSC recipients but were severely delayed beyond w8 in the HSC+Tc groups (particularly in CD4 recipients). Likewise, BM granulocyte recovery during the first 4w was significantly better in HSC vs. HSC+Tc recipients. Moreover, alloreactive (but not congenic) Tc severely disrupted the NHC: at 2w post allo-HCT HSC recipients showed prompt recovery of BM endothelial cells (EC; CD45-Ter119-CD31+), and CXCL12-abundant reticular cells (CAR; CD45-Ter119-CD31-CD140B+). In contrast, HSC+Tc recipients had significantly lower EC and CAR cells counts and their recovery was significantly delayed. 3D-confocal microscopy confirmed these observations and revealed rapid recovery of extracellular matrix and sinusoidal vascular structures, with simultaneous disappearance of adipocytes at 2w post-HCT in the HSC group, whereas HSC+Tc recipients displayed severe disruption of the structural integrity with impaired recovery of the BM microvessels but rather occupation of space by adipocytes. To visualize CAR cells by 3D imaging we used B6.CXCL12-GFP recipients and found, again, impaired CAR cell regeneration in HSC+Tc, but not pure HSC recipients. We next addressed the question whether alloreactive Tc exert direct GVH-related damage to NHC structures, or alternatively, only the recipient's HC is attacked whereas suppression of EC and CAR cells results from the inflammatory milieu. By transplanting C3H.SW BM into lethally irradiated B6 recipients chimeras with a C3H.SW HC and B6 NHC were generated. In a 2nd HCT HSC +/- Tc were transplanted from (i) C3H.SW (allo to NHC; syngeneic to HC) or (ii) B6 (congenic to NHC, allo to HC) donors into these chimeras. (i) At 2w post-2nd HCT BM cellularity, EC and CAR cell numbers were decreased when donor Tc were allogeneic to the NHC, but syngeneic to the HC. NHC populations were normal in number when pure allogeneic HSC (without Tc) were given. (ii) When grafts for 2nd HCT were from B6 donors (allo to HC, congenic to NHC), donor Tc had no effect on EC numbers. CAR cells were reduced in numbers, but less severely compared with after transfer of allogeneic HSC+Tc. Together, these findings suggest, that EC can be directly targeted by alloreactive Tc; CAR cells are affected by both, alloreactive Tc, and inflammatory conditions generated by attack of the HC. Finally, to test the influence of GVH-related cytokines on the NHC we used B6.TNFaR-/- and B6.IFNyR-/- mice as recipients of C3H.SW HSC +/- Tc. Recovery of HC and NHC was identical in WT and TNFaR-/- recipients of C3H.SW grafts. In contrast, IFNyR-/- recipients of allogeneic HSC+CD4 grafts had significantly higher CAR cell counts compared with WT.B6 recipients (but lower than recipients of pure HSC). These findings support the hypothesis that CAR cells are also susceptible to inflammatory damage.In clinical HCT delayed hematopoietic reconstitution presents a major problem contributing to increased morbidity and mortality. GVH reactions of the BM have been poorly characterized. Our data show that not only host hematopoiesis, but also non-hematopoietic stromal and vascular compartments of the BM are affected by both alloreactivity of donor Tc and their inflammatory microenvironment, even in the absence of clinical GVHD. DisclosuresNo relevant conflicts of interest to declare.

Recovery and reuse of carbon fibre and acrylic resin from thermoplastic composites used in marine application

The development of new sustainable routes towards better environmental footprint of composites for marine boatbuilding is motivated by the absence of eco-friendly and economically viable recycling techniques to deal with growing end-of-life thermoset boats waste and increasing restrictive legislation. Thermoplastic composites may offer the possibility to recover raw materials from retired parts, which could have a substantial environmental and economic benefit. This study investigates the feasibility of recovery and reuse of both reinforcement and matrix from a carbon fibre reinforced thermoplastic Elium composite. An original pyrolysis process allowed the depolymerisation of the acrylic matrix and recovery of clean carbon fibres and matrix monomer. Distillation was used to purify the monomer from impurities and a recycled Elium resin was synthesized and reused as matrix with long and aligned recycled carbon fibre reinforcement. The study of the morphological and composition differences at fibre scale resulted in concluding that sizing has been decomposed during depolymerisation process and only small traces of polymer were left on fibre surface. Virgin and recycled resin reinforced with carbon fibre laminates were produced by resin infusion. While tensile modulus, strength and strain at break were nearly unchanged, interlaminar shear strength results showed enhanced fibre-matrix interphase bonding. Dynamic mechanical analysis confirmed the good quality of recovered carbon fibres as well as similar virgin and re-polymerised resin properties. The new composite made from both recycled fibre and matrix could be used in the same application field.

Subspace estimation from unbalanced and incomplete data matrices: ℓ2,∞ statistical guarantees

This paper is concerned with estimating the column space of an unknown low-rank matrix A⋆∈Rd1×d2, given noisy and partial observations of its entries. There is no shortage of scenarios where the observations—while being too noisy to support faithful recovery of the entire matrix—still convey sufficient information to enable reliable estimation of the column space of interest. This is particularly evident and crucial for the highly unbalanced case where the column dimension d2 far exceeds the row dimension d1, which is the focal point of the current paper. We investigate an efficient spectral method, which operates upon the sample Gram matrix with diagonal deletion. While this algorithmic idea has been studied before, we establish new statistical guarantees for this method in terms of both ℓ2 and ℓ2,∞ estimation accuracy, which improve upon prior results if d2 is substantially larger than d1. To illustrate the effectiveness of our findings, we derive matching minimax lower bounds with respect to the noise levels, and develop consequences of our general theory for three applications of practical importance: (1) tensor completion from noisy data, (2) covariance estimation/principal component analysis with missing data and (3) community recovery in bipartite graphs. Our theory leads to improved performance guarantees for all three cases.

Effect of aging temperature on the heterogeneous microstructure and mechanical properties of a 12Cr–10Ni–Mo–Ti maraging steel for cryogenic applications

The evolution of heterogeneous microstructure and mechanical properties of a 12Cr–10Ni–Mo–Ti maraging steel was investigated at different aging temperatures. As the aging temperature increases, more reversed austenite forms with the recovery of martensite matrix. When aging temperature is up to 560 °C, more lath-like reversed austenite coalesces together and leads to the formation of martensite and austenite dual-phase microstructure. Fine η-Ni3(Ti, Al) particles initially precipitate at 440 °C and grow up with increased aging temperature. Notably, it was found that η-precipitates can be encompassed in reversed austenite at 500 °C. Thermodynamic calculations further verify the austenite reversion, and nanoprecipitation can occur independently of each other though there is competition of Ni element. However, it leads to the coarsening and dissolution of η-precipitates as the aging temperature further increases. The desirable heterogeneous microstructure of the aged martensite matrix, soft reversed austenite and stable η-precipitates at 500 °C contributes to a high yield strength (~ 1 GPa, 25 °C; ~ 1.4 GPa, -196 °C) and a reasonable cryogenic impact toughness (~ 60 J, − 196 °C), which is mainly ascribed to the precipitation strengthening of η-precipitates, transformation-induced plasticity (TRIP) toughening effect from the lath-like reversed austenite and the increased misorientation toward high-angle grain boundary.

HOPMCLDA: predicting lncRNA-disease associations based on high-order proximity and matrix completion.

In recent years, emerging evidence has shown that long noncoding RNAs (lncRNAs) have important roles in the biological processes of complex diseases. However, experiments to determine the associations between diseases and lncRNAs are time consuming and costly. Therefore, there is a need to develop effective computational methods for exploring potential lncRNA-disease associations. In this study, we present a computational prediction method based on high-order proximity and matrix completion to predict lncRNA-disease associations (HOPMCLDA). HOPMCLDA integrates explicit similarity and high-order proximity information on lncRNAs and diseases and constructs a heterogeneous disease-lncRNA network to utilize similarity information. Finally, nuclear norm regularization is carried out on the heterogeneous network for the recovery of a lncRNA-disease association matrix. By implementing leave-one-out cross validation (LOOCV) and five-fold cross validation (5-fold CV), we compare HOPMCLDA with five other methods. HOPMCLDA outperforms the other methods, with area under the receiver operating characteristic curve values of 0.8755 and 0.8353 ± 0.0045 using LOOCV and 5-fold CV, respectively. Furthermore, case studies of three human diseases (gastric cancer, osteosarcoma, and hepatocellular carcinoma) confirm the reliable predictive performance of HOPMCLDA.

An optimal condition of robust low-rank matrices recovery

In this paper we investigate the reconstruction conditions of nuclear norm minimization for low-rank matrix recovery. We obtain sufficient conditions $\delta_{tr}<t/(4-t)$ with $0<t<4/3$ to guarantee the robust reconstruction $(z\neq0)$ or exact reconstruction $(z=0)$ of all rank $r$ matrices $X\in\mathbb{R}^{m\times n}$ from $b=\mathcal{A}(X)+z$ via nuclear norm minimization. Furthermore, we not only show that when $t=1$, the upper bound of $\delta_r<1/3$ is the same as the result of Cai and Zhang \cite{Cai and Zhang}, but also demonstrate that the gained upper bounds concerning the recovery error are better. Moreover, we prove that the restricted isometry property condition is sharp. Besides, the numerical experiments are conducted to reveal the nuclear norm minimization method is stable and robust for the recovery of low-rank matrix.

Background modeling from video sequences via online motion-aware RPCA

Background modeling of video frame sequences is a prerequisite for computer vision applications. Robust principal component analysis(RPCA), which aims to recover low rank matrix in applications of data mining and machine learning, has shown improved background modeling performance. Unfortunately, The traditional RPCA method considers the batch recovery of low rank matrix of all samples, which leads to higher storage cost. This paper proposes a novel online motion-aware RPCA algorithm, named OM-RPCAT, which adopt truncated nuclear norm regularization as an approximation method for of low rank constraint. And then, Two methods are employed to obtain the motion estimation matrix, the optical flow and the frame selection, which are merged into the data items to separate the foreground and background. Finally, an efficient alternating optimization algorithm is designed in an online manner. Experimental evaluations of challenging sequences demonstrate promising results over state-of-the-art methods in online application.

Regularized Matrix Factorization for Multilabel Learning With Missing Labels

This article tackles the problem of multilabel learning with missing labels. For this problem, it is widely accepted that label correlations can be used to recover the ground-truth label matrix. Most of the existing approaches impose the low-rank assumption on the observed label matrix to exploit label correlations by decomposing it into two matrices, which describe the latent factors of instances and labels, respectively. The quality of these latent factors highly influences the recovery of ground-truth labels and the construction of the multilabel classification model. In this article, we propose recovering the ground-truth label matrix by regularized matrix factorization. Specifically, the latent factors of instances are regularized by the local topological structure derived from the feature space, which can be further used to induce an effective multilabel model. Moreover, the latent factors of labels and the label correlations are mutually adapted via label manifold regularization. In this way, the recovery of the ground-truth label matrix and the construction of the multilabel classification model are optimized jointly and can benefit from the regularized matrix factorization. Extensive experimental studies show that the proposed approach significantly outperforms the state-of-the-art algorithms on both full-label and missing-label data.

Age Hardening Characteristics of an Ultra-Low Carbon Cu Bearing Steel

We studied the influence of aging temperature on microstructure and mechanical properties in an ultra-low carbon Cu bearing steel in the present study. During the aging process, a continuous recovery of matrix associated with formation and growth of Cu precipitates could be observed during aging processes, exerting significant effects on the mechanical properties of the steel. At aging temperature below 600 °C, the mechanical properties were dominated by the precipitation strengthening effect, leading to excessive matrix strengthening and poor low-temperature toughness. Conversely, steel aged at temperatures above 650 °C exhibited an extraordinary improvement in toughness at the expense of strength, which can be attributed to the synergistic effects of softening matrix, coarsened Cu precipitates and formation of reverted austenite. After aging at 650 °C, reverted austenite formed at the lath boundaries. Increasing the aging temperature to 700 °C lowered the thermal stability of reverted austenite, consequently, the reverted austenite was partially transformed to fresh martensite. After aging at 650 °C for 0.5 h, the mechanical properties were optimized as follows—yield strength = 854 MPa, tensile strength = 990 MPa, elongation = 19.8% and Charpy impact energy = 132 J at −80 °C.

Hot Compression Mechanism and Comparative Study on Constitutive Models of Mo–3 vol%Al2O3 Alloys

The hot compression tests for Mo–3 vol.%Al2O3 alloys were conducted on a Gleeble-1500D thermo-mechanical simulator in the temperatures range of 1000–1300 °C and strain rates range of 0.005–1 s−1. The hot deformation behavior, mechanism associated with microstructure evolution of Mo–3 vol.%Al2O3 alloys was investigated by electron backscattering diffraction analysis. Three types of stress–strain curves were analyzed by quantifying the work hardening rate. The deformation mechanism at 1000–1300 °C mainly included the plastic deformation of Mo–3 vol.%Al2O3 alloy, as well as the dynamic recovery and recrystallization of Mo matrix. The modified Arrhenius, Modified Johnson–Cook and modified Zerilli–Armstrong constitutive equations were established and evaluated by the correlation coefficient (Rc) and average absolute relative error ( $${\bar{\text{e}}}$$ ). The flow stress of Mo–3 vol.%Al2O3 alloys could be well predicted by those three constitutive models, but modified Arrhenius constitutive model had a higher predicated accuracy. The hot deformation behavior, mechanism associated with microstructure evolution of Mo–3 vol.% Al2O3 alloys has been investigated by EBSD analysis. Three types of stress–strain curves were analyzed by quantifying the work hardening rate. The deformation mechanism at 1000–1300 °C mainly included the plastic deformation of Mo–3 vol.% Al2O3 alloy, as well as the dynamic recovery and recrystallization of Mo matrix. Moreover, modified Arrhenius constitutive model had a higher predicated accuracy for Mo–3 vol.% Al2O3 alloys than modified JC and modified ZA constitutive models.

Effect of pre-shearing on the steady and dynamic rheological properties of mud sediments

Mud sediments can exhibit a complex rheological behaviour particularly a thixotropic character or structural recovery after breakup due to the presence of organic matter/biopolymer. Such biopolymers can lead towards the development of flocculated structures having multiple length scales which are sensitive to shearing rate and history. In this study, the extent and rate of structural recovery of mud sediments was studied by measuring the storage modulus as a function of time using small amplitude oscillatory tests after a destructive steady shearing. This linear viscoelastic response of the sediments was further investigated as a function of several parameters including pre-shear rate, pre-shear time, measuring geometry, mud density and organic matter content. The equilibrium storage modulus (G∞') and the characteristic time (tr) for the structural recovery of the sediment matrix were estimated by fitting the experimental data to a stretched exponential function. The normalized storage modulus, G'/G0' (i.e., structural parameter) was used to relate it with the yield stresses of mud sediments. The results showed that the recovery of structure after shearing was instantaneous (tr being of the order of seconds), however, the extent of recovery was highly dependent on the studied parameters. The extent of recovery was higher for the samples with lower density and lower organic matter content. The effect of the shearing time on tr and G∞' was almost negligible, which implies that the destruction of the structure was achieved within seconds. Using vane geometry, the extent of recovery was higher than using Couette geometry which is linked with the distribution of shear stresses within the cell for each geometry. Yield stresses showed a strong dependency on structural parameter, until it reaches very small values. At low values of structural parameter, the yield stresses were constant as the structural recovery was even faster than the time required to perform the amplitude sweep tests. This study provides an extensive knowledge about the structural recovery in mud sediments under different shearing conditions which can be useful for sediment management.