Background and Aims: Disc displacement is the most common temporomandibular joint disorder and MAGNETIC resonance imaging (MRI) is the gold standard in its diagnosis. This disorder can lead to changes in signal intensity of MAGNETIC resonance (MR). The purpose of this study was evaluation of correlation between relative signal intensity of MR images of retrodiscal tissue, superior and inferior head of lateral ptrygoid muscle with type of anterior disk displacement and condylar head flattening in patients with temporomandibular disorder (TMD).Materials and Methods: In this retrospective study, 31 MR images of patients who had anterior disc displacement were evaluated. After relative signal intensity measurement for retrodiscal tissue, superior and inferior head of lateral ptrygoid muscle, the correlation between relative signal intensity and type of anterior disc displacement was evaluated with repeated measure ANOVA test. In each of these 3 areas, t-test was used to compare the groups with and without condylar head flattening.Results: The correlation between relative signal intensity of MR images and type of anterior disc displacement in retrodiscal tissue, superior and inferior head of lateral ptrygoid muscle was not significant. There was also no statistically significant correlation between relative signal intensity of MR images and flattening of condylar head in retrodiscal tissue, superior and inferior head of lateral ptrygoid muscle (P>0.05).Conclusion: According to findings of this study, relative signal intensity of MR images in retrodiscal tissue, superior and inferior head of ptrygoid muscle is not a good predictor for type of anterior disc displacement and flattening of condylar head. It seems that this cannot be used as a diagnostic marker for TMD progression.

The chaotic behavior of a flexible rotor supported by active MAGNETIC bearings is numerically investigated in this work. A statically unbalanced disk is mounted on the shaft. The rotor is modeled by three lumped mass and 8 D.O.F. The rotor-AMB systems include many non-linear factors, such as nonlinear function of the coil current and the air gap between the rotor and the stator, nonlinearity due to geometric coupling of MAGNETIC actuator, eddy current effect and hysteresis losses of the MAGNETIC core material. In this work, the influence of weight PARAMETER on nonlinear response of the system is investigated. Numerical results showed considering weight PARAMETER has an important effect on the response of the rotor and exhibits a rich variety of nonlinear dynamical behavior including synchronous, sub-synchronous, quasi-periodic and chaotic vibrations. Bifurcation diagrams, phase planes, power spectra, Poincare map and maximum Lyapanov exponents are used to analyze the response of the system under different operational conditions. Chaotic vibrations should be avoided as they induce fluctuating stresses that may lead to premature failure of the machinery’s main component. It will be beneficial to the design of AMB system.

In order to analyze the functional MAGNETIC Resonance Imaging (fMRI) data, the PARAMETERs of a nonlinear model for the hemodynamic system, so called Balloon model, were characterized and estimated. Two different approaches were applied to estimate these PARAMETERs. In the first step of both approaches, the voxels which show neural activity were identified. Then, the PARAMETERs of the balloon model for these active voxels were estimated by both steepest descent algorithm, and through genetic algorithm. Proposed approaches were applied on experimental fMRI data and the PARAMETERs of nonlinear Balloon model were estimated for different brain voxels. Accuracy of these characterizations was assessed via comparing the measured time series at each voxel with the modeled time series. Also, it was shown that the results of the PARAMETER-estimation are consistent with the results obtained from system characterization via Volterra Kernels (which were reported in previous studies). It was concluded that the suggested approaches could accomplish a nonlinear system characterization through numerical methods, whereas they avoid theoretical complexities and they have acceptable speed (especially steepest descent algorithm).

In this study, the stability of the combined PSO-GA algorithm in estimating MAGNETIC model PARAMETERs is evaluated and compared with two other algorithms: Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The PSO algorithm is employed to enhance the action vector, while the GA algorithm is applied to rectify the decision vectors. This amalgamation of algorithms, utilizing genetic operators, enhances exploration and exploitation capabilities. The optimization algorithm exhibits the potential to be applied in the exploration and estimation of mineral reservoirs, offering a rapid method for simulating MAGNETIC anomalies based on ideal geological models. Moreover, in geophysical investigations, it is customary to employ modeling techniques using standard geometric shapes like spheres, cylinders, vertical prisms, dikes, and similar forms to assess MAGNETIC anomaly attributes. Furthermore, in current research, the algorithm's performance is examined by introducing Gaussian white noise to synthetic data, demonstrating its effectiveness even when faced with noise levels as high as 25%. Additionally, authentic airborne MAGNETIC data from the Basiran region in South Khorasan province are applied to validate the model, confirming its consistency with geological findings.

In this paper a fast method for large-scale sparse inversion of MAGNETIC data is considered. The L1-norm stabilizer is used to generate models with sharp and distinct interfaces. To deal with the nonlinearity introduced by the L1-norm, a model-space iteratively reweighted least squares algorithm is used. The original model matrix is factorized using the Golub-Kahan bidiagonalization that projects the problem onto a Krylov subspace with a significantly reduced dimension. The model matrix of the projected system inherits the ill-conditioning of the original matrix, but the spectrum of the projected system accurately captures only a portion of the full spectrum. Equipped with the singular value decomposition of the projected system matrix, the solution of the projected problem is expressed using a filtered singular value expansion. This expansion depends on a regularization PARAMETER which is determined using the method of Generalized Cross Validation (GCV), but here it is used for the truncated spectrum. This new technique, Truncated GCV (TGCV), is more effective compared with the standard GCV method. Numerical results using a synthetic example and real data demonstrate the efficiency of the presented algorithm.