Abstract A non-linear poroelastic finite element model of the lumbar spine was developed to investigate spinal response during daily dynamic physiological activities. Swelling was simulated by imposing a boundary pore pressure of 0.25 MPa at all external surfaces. Partial saturation of the disc was introduced to circumvent the negative pressures otherwise computed upon unloading. The loading conditions represented a pre-conditioning full day followed by another day of loading: 8 h rest under a constant compressive load of 350 N, followed by 16 h loading phase under constant or cyclic compressive load varying in between 1000 and 1600 N. In addition, the effect of one or two short resting periods in the latter loading phase was studied. The model yielded fairly good agreement with in-vivo and in-vitro measurements. Taking the partial saturation of the disc into account, no negative pore pressures were generated during unloading and recovery phase. Recovery phase was faster than the loading period with equilibrium reached in only ∼3 h. With time and during the day, the axial displacement, fluid loss, axial stress and disc radial strain increased whereas the pore pressure and disc collagen fiber strains decreased. The fluid pressurization and collagen fiber stiffening were noticeable early in the morning, which gave way to greater compression stresses and radial strains in the annulus bulk as time went by. The rest periods dampened foregoing differences between the early morning and late in the afternoon periods. The forgoing diurnal variations have profound effects on lumbar spine biomechanics and risk of injury.
The windings of power synchronous machines are often parallel connected to obtain the desired machine voltage and current ratings. Ideally, the split-phase currents are equal in symmetrical windings, so as to avoid parasitic circulation of current in the parallel branches. However, the symmetry is broken in case of rotor eccentricity, and the split-phase currents become unbalanced. Part I of this paper analyzed the theoretical behavior of the unbalanced currents by using symmetrical components. A new fault diagnosis method was shown, based on a combined space-vector/fast Fourier transformation (FFT) analysis of signatures in the split-phase currents. This Part II applies the split-phase current signature analysis to a laboratory 17-kVA synchronous generator with artificial faults. Method performances have been evaluated with mixed static/dynamic type fault, in no-load and loaded conditions. The experiments are matched with time-stepping finite-element simulations, which help explain the effect of saturation and load on the diagnosis accuracy. The feasibility of installation of current probes in practical machines is also discussed.
We explain four variants of an adaptive finite element method with cubic splines and compare their performance in simple elliptic model problems. The methods in comparison are Truncated Hierarchical B-splines with two different refinement strategies, T-splines with the refinement strategy introduced by Scott et al. in 2012, and T-splines with an alternative refinement strategy introduced by some of the authors. In four examples, including singular and non-singular problems of linear elasticity and the Poisson problem, the H1-errors of the discrete solutions, the number of degrees of freedom as well as sparsity patterns and condition numbers of the discretized problem are compared. The quantitative results of this paper can help the reader to choose the most appropriate refinement method for the application.
In this brief, multilinear sparse principal component analysis (MSPCA) is proposed for feature extraction from the tensor data. MSPCA can be viewed as a further extension of the classical principal component analysis (PCA), sparse PCA (SPCA) and the recently proposed multilinear PCA (MPCA). The key operation of MSPCA is to rewrite the MPCA into multilinear regression forms and relax it for sparse regression. Differing from the recently proposed MPCA, MSPCA inherits the sparsity from the SPCA and iteratively learns a series of sparse projections that capture most of the variation of the tensor data. Each nonzero element in the sparse projections is selected from the most important variables/factors using the elastic net. Extensive experiments on Yale, Face Recognition Technology face databases, and COIL-20 object database encoded the object images as second-order tensors, and Weizmann action database as third-order tensors demonstrate that the proposed MSPCA algorithm has the potential to outperform the existing PCA-based subspace learning algorithms.
There are no studies about the biomechanical analysis of lumbar decompression surgery in relation to degenerative changes of the lumbar spine. Therefore, the purpose of this study was to compare, by using finite element (FE) analysis, the biomechanical changes of the lumbar spine in terms of annulus stress and nucleus pressure after two different kinds of lumbar decompression surgery in relation to disc degenerative changes. The validated intact and degenerated FE models (L2-5) were used in this study. In these two models, two different decompression surgical scenarios at L3-4, including conventional laminectomy (ConLa) and the spinous process osteotomy (SpinO), were simulated. Therefore, a total of six models were simulated. Under preloading, 7.5 Nm moments of flexion, extension, lateral bending, and torsion were imposed. In each model, the maximal von Mises stress on the annulus fibrosus and nucleus pressure at the index segment (L3-4) and adjacent segments (L2-3 and L4-5) were analyzed. The ConLa model and disc degeneration model demonstrated a larger annulus stress at the decompression level (L3-4) under all four moments than were seen in the SpinO model and healthy disc model, respectively. Therefore, the ConLa model with moderate disc degeneration showed the highest annulus stress at the decompression level (L3-4). However, the percent change of annulus stress at L3-4 from the intact model to the matched decompression model was less in the moderate disc degeneration model than in the healthy disc model. Although the ConLa model with moderate disc degeneration showed the highest annulus stress, the degenerative models would be less influenced by the decompression technique.
Osteocytes are hypothesized to regulate bone remodeling guided by both biological and mechanical stimuli. Morphology of the lacunar-canalicular network of osteocytes has been hypothesized to be strongly related to the level of mechanical loading and to various bone diseases. Finite element modeling could help to better understand the mechanosensation process by predicting the physiological strain environment. The aims of this study were to (i) quantify the lacunar-canalicular morphology in the cortex of the human femur; (ii) predict the in situ local deformations around and in osteocytes by means of case-specific finite element models; and (iii) investigate the potential relationship between morphology and deformations. Human femoral cortical bone samples were imaged using synchrotron X-ray phase nano-tomography with 50 nm voxel size. Rectangular volumes of interest were selected to contain single osteocyte lacunae and the surrounding matrix. Lacunar-canalicular morphology was quantified and the cell geometry was artificially reconstructed based on a priori assumptions. Finite element models of the volumes of interest were generated, containing the extracellular matrix, osteocyte and peri-cellular matrix, and subjected to uniaxial compression. The morphological analysis revealed that canalicular number was dictated by lacunar size, that the spacing of canaliculi fell within a narrow range, suggesting that these pores are well distributed throughout the bone matrix and indicated the trend that lacunae at the outer osteon boundary were less elongated than others. No apparent relationship was found between the morphological parameters and the predicted strains. The globally applied strain was amplified locally by factors up to 10 and up to 70 in the extracellular matrix and the in cells, respectively. Cell deformations were localized mainly at the body-dendrite junctions, with magnitudes reaching the in vitro stimulatory threshold reported for osteocytes.
With the increasing availability of various sensor technologies, we now have access to large amounts of multiblock (also called multiset, multirelational, or multiview) data that need to be jointly analyzed to explore their latent connections. Various component analysis methods have played an increasingly important role for the analysis of such coupled data. In this article, we first provide a brief review of existing matrix-based (two-way) component analysis methods for the joint analysis of such data with a focus on biomedical applications. Then, we discuss their important extensions and generalization to multiblock multiway (tensor) data. We show how constrained multiblock tensor decomposition methods are able to extract similar or statistically dependent common features that are shared by all blocks, by incorporating the multiway nature of data. Special emphasis is given to the flexible common and individual feature analysis of multiblock data with the aim to simultaneously extract common and individual latent components with desired properties and types of diversity. Illustrative examples are given to demonstrate their effectiveness for biomedical data analysis.