Shape Matrix Research Articles

Composites Based on Thermoplastic Polymeric Matrix and Carbon Nanoparticles with Special Functional Properties

Polymer composite materials based on polypropylene matrix and carbon nanoparticles of various shapes: spherical particles - carbon black and anisotropic nanoparticles - carbon nanofibers, were obtained by the melt technology. A character of the dependences of the electrical conductivities on the concentration of carbon nanofillers has been revealed. A simulation of the electrical conductivity of the polymer composite in the region of the concentration of the filler at the threshold of the flow of electric current and below it was carried out.

Design of Intelligent Medicine Box for Elderly People Based on Context Awareness and FAST Theory

Based on context awareness and FAST (Function Analysis System Technique) theory, this paper explored the needs of elderly users and designed an intelligent medicine box for them to provide more comprehensive health services. It defined context through context analysis method, combined four context factors to obtain human-machine interaction model, and built a functional tree through the FAST theory to analyze product design factors and functional transformation forms. Finally, this paper established a product shape matrix upon functional combination and human-machine relationship, and selected the best design scheme. Through a practical application to a case, the validity of context awareness and FAST theory hybrid approach and their technical feasibility and effectiveness was demonstrated, and an accurate mapping relationship between objective product performance and user needs was formed.

Substrate stiffness- and topography-dependent differentiation of annulus fibrosus-derived stem cells is regulated by Yes-associated protein

Annulus fibrosus (AF) tissue engineering has attracted increasing attention as a promising therapy for degenerative disc disease (DDD). However, regeneration of AF still faces many challenges due to the tremendous complexity of this tissue and lack of in-depth understanding of the structure-function relationship at cellular level within AF is highly required. In light of the fact that AF is composed of various types of cells and has gradient mechanical, topographical and biochemical features along the radial direction. In this study, we aimed to achieve directed differentiation of AF-derived stem cells (AFSCs) by mimicking the mechanical and topographical features of native AF tissue. AFSCs were cultured on four types of electrospun poly(ether carbonate urethane)urea (PECUU) scaffolds with various stiffness and fiber size (soft, small size; stiff, small size; soft, large size and stiff, large size). The results show that with constant fiber size, the expression level of the outer AF (oAF) phenotypic marker genes in AFSCs increased with the scaffold stiffness, while that of inner AF (iAF) phenotypic marker genes showed an opposite trend. When scaffold stiffness was fixed, the expression of oAF phenotypic marker genes in AFSCs increased with fiber size. While the expression of iAF phenotypic marker genes decreased. Such substrate stiffness- and topography-dependent changes of AFSCs was in accordance with the genetic and biochemical distribution of AF tissue from the inner to outer regions. Further, we found that the Yes-associated protein (YAP) was translocated to the nucleus in AFSCs cultured with increasing stiffness and fiber size of scaffolds, yet it remained mostly phosphorylated and cytosolic in cells on soft scaffolds with small fiber size. Inhibition of YAP down-regulated the expression of tendon/ligament-related genes, whereas expression of the cartilage-related genes was upregulated. The results illustrate that matrix stiffness is a potent regulator of AFSC differentiation. Moreover, we reveal that fiber size of scaffolds induced changes in cell adhesions and determined cell shape, spreading area, and extracellular matrix expression. In all, both mechanical property and topography features of scaffolds regulate AFSC differentiation, possibly through a YAP-dependent mechanotransduction mechanism. Statement of SignificancePhysical cues such as mechanical properties, topographical and geometrical features were shown to profoundly impact the growth and differentiation of cultured stem cells. Previously, we have found that the differentiation of annulus fibrosus-derived stem cells (AFSCs) could be regulated by the stiffness of scaffold. In this study, we fabricated four types of poly(ether carbonate urethane)urea (PECUU) scaffolds with controlled stiffness and fiber size to explore the potential of induced differentiation of AFSCs. We found that AFSCs are able to present different gene expression patterns simply as a result of the stiffness and fiber size of scaffold material. This work has, for the first time, demonstrated that larger-sized and higher-stiffness substrates increase the amount of vinculin assembly and activate YAP signaling in pre-differentiated AFSCs. The present study affords an in-depth comprehension of materiobiology, and be helpful for explain the mechanism of YAP mechanosensing in AF in response to biophysical effects of materials.

A spatial rank–based multivariate EWMA chart for monitoring process shape matrices

AbstractIn this article, we propose a nonparametric EWMA control chart for monitoring the shape matrix of a multivariate process based on a spatial rank test and the exponentially weighted moving average scheme. The proposed control chart is essentially developed using an estimated spatial rank covariance matrix to test the shape matrix of the covariance matrix of multivariate distributions with heavy tails. Based on our simulation studies, the proposed control chart outperforms the only existing nonparametric control chart in many practical out‐of‐control scenarios for monitoring the shape matrix of the covariance matrix of many multivariate processes. Further, we point out the weaknesses of both the nonparametric EWMA control charts for monitoring the shape matrix of multivariate processes in real applications and propose one possible method to overcome these weaknesses. We also use an example from a white wine production process to demonstrate the applicability and implementation of the proposed control chart.

An Ultrasonic Guided Wave Mode Selection and Excitation Method in Rail Defect Detection

Different guided wave mode has different sensitivity to the defects of rail head, rail web and rail base in the detection of rail defects using ultrasonic guided wave. A novel guided wave mode selection and excitation method is proposed, which is effective for detection and positioning of the three parts of rail defects. Firstly, the mode shape data in a CHN60 rail is obtained at the frequency of 35 kHz based on SAFE method. The guided wave modes are selected, combining the strain energy distribution diagrams with the phase velocity dispersion curves of modes, which are sensitive to the defects of the rail head, rail web and rail base. Then, the optimal excitation direction and excitation node of the modes are calculated with the mode shape matrix. Phase control and time delay technology are employed to achieve the expected modes enhancement and interferential modes suppression. Finally, ANSYS is used to excite the specific modes and detect defects in different rail parts to validate the proposed methods. The results show that the expected modes are well acquired. The selected specific modes are sensitive to the defects of different positions and the positioning error is small enough for the maintenance staff to accept.

Intrinsic Cramér–Rao Bounds for Scatter and Shape Matrices Estimation in CES Distributions

Scatter matrix and its normalized counterpart, referred to as shape matrix, are key parameters in multivariate statistical signal processing, as they generalize the concept of covariance matrix in the widely used Complex Elliptically Symmetric distributions. Following the framework of [1], intrinsic Cramér-Rao bounds are derived for the problem of scatter and shape matrices estimation with samples following a Complex Elliptically Symmetric distribution. The Fisher Information Metric and its associated Riemannian distance (namely, CES-Fisher) on the manifold of Hermitian positive definite matrices are derived. Based on these results, intrinsic Cramér-Rao bounds on the considered problems are then expressed for three different distances (Euclidean, natural Riemannian, and CES-Fisher). These contributions are therefore a generalization of Theorems 4 and 5 of [1] to a wider class of distributions and metrics for both scatter and shape matrices.

Depth matrix and adaptive Bayes classifier based dynamic hand gesture recognition

A sequence of apparently ad-hoc hand postures can generate meaningful dynamic gestures which can be utilized in interface controls for computer, television, or games. In order to develop deployable systems with these gestures, selected descriptors should be fast enough to meet the live recognition requirements. This paper proposes framework for a practical system capable of recognizing continuous dynamic gestures characterized by short-duration posture sequences. A depth-based modification to the shape matrix is devised to describe hand silhouettes, which gives a faster alternative to region-based descriptors. Postures are recognized using depth matrix and 1-nearest neighbor strategy. Posture sequence labels are predicted by a dynamic naive Bayes classifier which works in association with an adaptive windowing mechanism. The conducted experiments report up to 96.2% accurate results with mean accuracy of 95.2% on dynamic gesture dataset. Depth matrix computation takes a maximum of 2ms time.

Ultrasound guided tendon therapies: Options and evidence

Tendon injury is very common, particularly in active people. Common examples include pathology of the rotator cuff, common extensor and flexor tendons at the elbow, gluteal tendons at the greater trochanter, patellar tendon and Achilles tendon. Healthy tendon has an ordered structure that includes uniform flat-shaped tenocyte cells, collagen fibers organised and layered end-to-end and side-by-side, and a structurally organized extracellular matrix (ECM). Response of tendon to injury is a complex pathological process for which the term ‘tendinosis’ is used. This process consists of early disruption of collagen fibre orientation, followed by cell proliferation, angiogenesis, change in tenocyte cell shape, and collagen and extracellular matrix breakdown. Tendon cell death and depletion at the centre of the affected region leads to further collagen and extracellular matrix breakdown, followed by fatigue healing and degenerative changes. Treatment options for tendon pathologies include conservative measures such as modification of behavior, strapping, oral anti-inflammatories, and lithotripsy. Ultrasound guided injection techniques include HCLA into, or adjacent to, the tendon, dry needling of the affected tendon, autologous blood injection, platelet rich plasma (PRP) and autologous tenocyte injection (ATI). Surgical options such as debridement, division or reconstruction of the affected tendon may be used when conservative treatments have failed. ATI is a cell-based therapy that involves the harvesting of healthy tendon from the patient. The autologous donor tendon material is obtained using ultrasound guided percutaneous core biopsy, usually of the quadriceps or patellar tendon. Adequate volume of tendon, sterile technique and minimal trauma to the healthy tendon and adjacent tissues is essential. This material is cultured for 6 weeks to produce a suspension of tenocytes, which are injected under ultrasound guidance into the abnormal tendon. The aim of the procedure is to grow new tendon tissue within the abnormal structure, restore function and relieve pain. The presence of multiple available treatments implies that no single treatment is reliably effective. Various treatments may be advocated based on multiple factors such as individual experience and expertise, plus local availability. A review of these treatments will be presented, with an attempt to make sense of the currently available evidence.

Modal Participation Estimated from the Response Correlation Matrix

In this paper, we are considering the case of estimating the modal participation vectors from the operating response of a structure. Normally, this is done using a fitting technique either in the time domain using the correlation function matrix or in the frequency domain using the spectral density matrix. In this paper, a more simple approach is proposed based on estimating the modal participation from the correlation matrix of the operating responses. For the case of general damping, it is shown how the response correlation matrix is formed by the mode shape matrix and two transformation matrices T1 and T1 that contain information about the modal parameters, the generalized modal masses, and the input load spectral density matrix Gx. For the case of real mode shapes, it is shown how the response correlation matrix can be given a simple analytical form where the corresponding real modal participation vectors can be obtained in a simple way. Finally, it is shown how the real version of the modal participation vectors can be used to synthesize empirical spectral density functions.

The scheme of the algorithm for the step-by-step reduction of two matrices in the form of Schur to a simple form

Of great importance for modern technology are the issues of the dynamic behavior of machines and structures. Given the trends of today such as new information technologies, the transformation of design processes; creation of CAD systems (automation systems for design work), issues of improving methods for solving dynamics problems are of particular importance.Numerical modeling has become an integral part of the study of the most complex processes for the most complex physical models, and the Finite Element Method has become the main numerical method for their study. These trends will inevitably be accompanied by a significant increase in the number of parameters for determining the state of the object. In particular, in the dynamics of machines, a significant number of degrees of freedom. As a result, the appearance of the problem of a multiple increase in the size of the task. The solution of the eigenvalue problem (EigenValue) can be considered as an important component in the construction of numerical-analytical approaches that are alternative to simple step-by-step integration schemes of the Runge-Kutta type in large-scale problems. Certain benefits can be obtained if the linear transformation matrices are first reduced to a simple form. This approach is widely used in dynamics (modal analysis). In this paper, we propose a scheme for a step-by-step numerical analysis of the structure of the matrix K in a problem and a scheme for constructing a Jordan basis for the general case of roots of a characteristic polynomial (for real and complex roots, simple and multiple). As the starting form, the standard eigenvalue problem with the matrix K previously reduced to the Schur form (block-triangular shape matrix) is adopted. The scheme is accompanied by the solution of test cases.

958: The harvesting, transplant, and tracking of stem cell sheets for the prevention of uterine scarring

Rates of placenta accreta are increasing along with increased rates of cesarean. Thus, we assessed the feasibility and reproducibility of creating, transplanting and tracking stem cell sheets (SCS) onto a hysterotomy in a rat model. Cell Sheet Tissue Engineering Center at University of Utah has focused on allogenic cell sheet therapy using cell sheet tissue engineering developed by T. Okano. Human umbilical cord mesenchymal stem cells (hUC-MCS) were seeded in a nutritional media, promoting their division and increased their concentration. After reaching a confluence, the cell suspension was seeded on a thermo-responsive cell culture dishes (UpCell®) at a concentration of 3.0 x105 cells/dish. These dishes have a polymer at the bottom that changes its characteristic with fluctuation in temperature. At 37°C the polymer is hydrophobic, the seeded cells will attach to its surface and multiply to confluence of cells. At 20°C the polymer becomes hydrophilic, detaching the cells from its surface in the form of a SCS. In this process, an adhesive extracellular matrix forms on the basal side of the cell sheets. The now floating SCS are scooped using a thin plastic square sheet, which is used to transport the SCS to the surgical field (Figure 1). SCS cells are labeled with a fluorescent marker to allow them to be identified after transplantation. After hysterotomy repair in rat models, the SCS is transplanted on the surface of the hysterotomy by sliding it down from the plastic sheet with forceps. The shape and adhesive extracellular matrix of SCS allows it to be fixated directly onto the hysterotomy without any scaffolds or sutures. Proper positioning and location of the SCS is confirmed by fluorescent microscopy and evaluated on days one, three, and seven after surgery. A total of 3 SCS were created from one vial of stem cell suspension, with surface areas of approximately 1cm2 (Figure1). All sheets were successfully transplanted in 3 rats that underwent hysterotomy and repair. In the follow up period, we were able to localize the SCS in healed rat uteruses in the transplanted location (Figure 2). Stem cell laying in sheet form is a feasible and reproducible technology in which the transplantation of stem cells can be made directly to the required location.View Large Image Figure ViewerDownload Hi-res image Download (PPT)

Estimation of mass matrix in machine tool’s weak components research by using symbolic regression

Modal mass is the important dynamic parameter in weak component research of machine tool structure and also for its control design and load design. Modal mass matrix is defined as the multiplication of mass matrix of a machine tool and its corresponding modal shape matrix. Currently, the big problem is that the mass matrix is hard to get for the calculation of modal mass matrix. Traditional method such as the finite element method cannot acquire the mass matrix well because the overall mass matrix of complex systems cannot be given by experience and the mass matrix in finite element analysis is so large that the computer hard disk will be blasted. In addition to finite element method, the UMM method is used commonly but the noise contained in the mode of the data processing is mixed into the mass matrix, resulting in the inaccurate result and even failure in severe cases. So, there is an urgent need for a method of directly obtaining the mass matrix based on a general equation of multi-degree-of-freedom vibration system from a data source, and then used to calculate the modal mass. In this paper, Genetic programming algorithm (GP) in symbolic regression as an evolution computation method is used to search out the equation expression structure and its coefficients among a group of variances including displacement, velocity, acceleration and external excitation force. And the mass matrix is contained in the equations’ coefficients. In addition, its performance is compared with LRA method and PSO method.

Optimal sensor placement and assessment for modal identification

A review is presented on a wide range of optimal sensor placement (OSP) strategies and ways of evaluation. An assessment-guided optimal sensor placement algorithm based on minimal root mean square is proposed to give quantitative comparison on these options. The algorithm is applied to an example of a cantilever beam, as well as to a jacket-platform where model uncertainty and measurement error are included simultaneously. The results reveal the influence of the number of desired sensors and the number of modes of interest. It is shown that the optimal sensor placement selected by the proposed strategy yields the best modal orthogonality of the corresponding mode shapes, and competitive results are derived on the other two assessment standards, i.e. the condition number of the reduced mode shape matrix and the determinant of the Fisher information matrix. The conclusions also show that the effective independence method keeps the largest determinant, and there seems no regular trend on the condition number but unexpected biased solutions can be derived when unreasonable combination for number of wanted sensors and modes of interest are chosen. The proposed minimal root mean square algorithm is capable of avoiding this drawback.

B-WIM SYSTEM USING FEWER SENSORS

Bridge Weigh-in-Motion (B-WIM) is the concept of using measured response on a bridge to calculate the static weights of passing traffic loads as they pass overhead at full highway speed. This paper describes an enhancement to the Moving Force Identification (MFI) algorithm by estimating the response of some DOFs using limited number of measurements in order to increase measurements number (Input). The pseudoinverse of the mode shape matrix has been utilized to approximately calculate the modal response using limited measured response. Then the calculated modal response has been used to estimate more DOFs that are different from the measured one. The proper orthogonal decomposition (POD) technique is employed to determine the governing modes that increase the modal response accuracy. Numerical example for quarter car model passing over simply supported bridge has been established to demonstrate the idea.

Micromechanics of stress transfer in shape memory alloy reinforced three-phase composites

Due to the weak interface in shape memory alloy wire–reinforced composites, the influence of interphase on the mechanical properties and stress distribution of hybrid composites is of considerable importance. In this article, a three-cylinder axisymmetric model using a pull-out test is developed to predict stress transfer and interfacial behavior between shape memory alloy wire, interphase, and matrix. In this article, only superelasticity behavior of the shape memory alloy wire is considered. Based on the stress function method and the principle of minimum complementary energy, stress distribution is derived for three different cases in terms of loading and boundary conditions (thermal loading model, intact model, and partially debonded model). Inhomogeneous interphase and different radial and hoop stress components in each phase are considered to achieve deeper physical understanding. Finite element analysis also performed to simulate stress transfer from the wire to the matrix through the interphase. To evaluate the accuracy of this model, the results of the work are compared with the results of the two-cylinder model proposed by Wang et al. and finite element results.

Development of a stochastic effective independence (SEFI) method for optimal sensor placement under uncertainty

Abstract Finding optimal sensor placement for data acquisition is an essential initial stage in the development of many engineered systems. For this purpose, the effective independence (EFI) method was developed and has been widely used. In this method, sensor locations are selected to maximize the linear independency of the target mode shape matrix. However, the EFI method lacks consideration of the uncertainty that is present in real applications. To overcome this limitation, in this study, a new stochastic EFI method is derived. The resultant equation is composed of the conventional deterministic term and an additional random term. Using the derived equation, optimal sensor locations are found. The results of stochastic EFI give the best linear independency of the mode shape matrix in the mean sense. In this paper, stochastic EFI is also extended to the energy-based EFI method. In the energy-based EFI method, the mass or stiffness matrix is weighted to have the kinetic or strain energy form of the EFI method. By decomposing the weighted matrix, the same form is obtained as in the EFI method; thus, its stochastic version follows naturally, as in the stochastic EFI method. Further, the stochastic sensor placement method is also derived for a different optimization criterion called the A-optimality criterion, which uses a matrix trace for its measure. Finally, the proposed method is validated using a truss bridge case. Its results are compared with the Monte Carlo simulation based method, which is another approach used to handle the system uncertainty. The case study indicates that the suggested method shows higher linear independency than the EFI method and the energy-based EFI method; it is also better with different optimality conditions.

The Extracellular Matrix of Ovarian Cortical Inclusion Cysts Modulates Invasion of Fallopian Tube Epithelial Cells.

A growing body of research supports the idea that the fallopian tube epithelium (FTE) is the precursor for most high-grade serous ovarian cancers (HGSOCs) but that the ovary plays a critical role in tumor metastasis. Cortical inclusion cysts (CICs) in the ovarian cortex have been hypothesized to create a niche environment that plays a role in HGSOC progression. Through histological analysis of pathology samples from human ovaries, we determined that collagen I and III were elevated near CICs and that the collagen fibers in this dense region were oriented parallel to the cyst boundary. Using this information from human samples as design parameters, we engineered an in vitro model that recreates the size, shape, and extracellular matrix properties of CICs. We found that FTE cells within our model underwent robust invasion that was responsive to stimulation with follicular fluid, while ovarian surface epithelial cells, the native cells of the ovary, were not invasive. We provide experimental evidence to support a role of the extracellular matrix in modulating FTE cell invasion, as a decrease in collagen I concentration or the addition of collagen III to the matrix surrounding FTE cells increased FTE cell invasion. Taken together, we show that an in vitro model of CICs obtained from the analysis of human tissue can act as an important tool for understanding FTE cell interactions with their environment.

MRI features predictive of negative surgical margins in patients with HER2 overexpressing breast cancer undergoing breast conservation

Here we develop a tool to predict resectability of HER2+ breast cancer at breast conservation surgery (BCS) utilizing features identified on preoperative breast MRI. We identified patients with HER2+ breast cancer who obtained pre-operative breast MRI and underwent BCS between 2002–2013. From the contoured tumor on pre-operative MRI, shape, histogram, and co-occurrence and size zone matrix texture features were extracted. In univariate analysis, Spearman’s correlation coefficient (Rs) was used to assess the correlation between each image feature and an endpoint (surgical re-excision). For multivariate modeling, we employed a support vector machine (SVM) method in a manner of leave-one-out cross-validation (LOOCV). Of 109 patients with HER2+breast cancer who underwent BCS, 39% underwent surgical re-excision. 62% had residual cancer at re-excision. In univariate analysis, solidity (Rs = −0.32, p = 0.009) and extent (Rs = −0.29, p = 0.019) were significantly associated with re-excision. Skewness in post-contrast 1, 2, and 3 (Rs = 0.25, p = 0.045; Rs = 0.30, p = 0.015; Rs = 0.28, p = 0.026) and kurtosis in post-contrast 1 (Rs = 0.26, p = 0.035) were also statistically significant. LOOCV-based SVM test achieved 74.4% specificity and 71.4% sensitivity when 21 features were used. Thus, tumor texture, histogram and morphological MRI features may assist surgical planning, encouraging wide margins or mastectomy in patients who may otherwise go on to re-excision.

Proximal robust factorization for piecewise planar reconstruction

In this paper, we aim to obtain a dense piecewise planar reconstruction of the scene from multiple image frames based on a factorization framework. Integrating all the relevant constraints in a global objective function, we are able to effectively leverage on the scene smoothness prior afforded by the dense formulation, as well as imposing the necessary algebraic constraints required by the shape matrix. These constraints also help to robustly decompose the measurement matrix into the underlying low-rank subspace and the sparse outlier part. Numerically, we achieve the constrained factorization and decomposition via modifying a recently proposed proximal alternating robust subspace minimization algorithm. The results show that our algorithm is effective in handling real life sequences, and outperforms other algorithms in recovering motions and dense scene estimate.

Mechanobiology of YAP and TAZ in physiology and disease.

A growing body of evidence suggests that mechanical signals emanating from the cell's microenvironment are fundamental regulators of cell behaviour. Moreover, at the macroscopic scale, the influence of forces, such as the forces generated by blood flow, muscle contraction, gravity and overall tissue rigidity (for example, inside of a tumour lump), is central to our understanding of physiology and disease pathogenesis. Still, how mechanical cues are sensed and transduced at the molecular level to regulate gene expression has long remained enigmatic. The identification of the transcription factors YAP and TAZ as mechanotransducers started to fill this gap. YAP and TAZ read a broad range of mechanical cues, from shear stress to cell shape and extracellular matrix rigidity, and translate them into cell-specific transcriptional programmes. YAP and TAZ mechanotransduction is critical for driving stem cell behaviour and regeneration, and it sheds new light on the mechanisms by which aberrant cell mechanics is instrumental for the onset of multiple diseases, such as atherosclerosis, fibrosis, pulmonary hypertension, inflammation, muscular dystrophy and cancer.