Application of an airfoil stall flutter computer prediction program to a three-dimensional wing: Prediction versus experiment

An aerodynamic computer code, capable of predicting unsteady and C sub m values for an airfoil undergoing dynamic stall, is used to predict the amplitudes and frequencies of a wing undergoing torsional stall flutter. The code, developed at United Technologies Research Corporation (UTRC), is an empirical prediction method designed to yield unsteady values of normal force and moment, given the airfoil's static coefficient characteristics and the unsteady aerodynamic values, alpha, A and B. In this experiment, conducted in the PSU 4' x 5' subsonic wind tunnel, the wing's elastic axis, torsional spring constant and initial angle of attack are varied, and the oscillation amplitudes and frequencies of the wing, while undergoing torsional stall flutter, are recorded. These experimental values show only fair comparisons with the predicted responses. Predictions tend to be good at low velocities and rather poor at higher velocities

Decompressive cervical laminectomy and lateral mass screw-rod arthrodesis. Surgical analysis and outcome

<p>Abstract</p> <p>Background</p> <p>This study evaluates the outcome and complications of decompressive cervical Laminectomy and lateral mass screw fixation in 110 cases treated for variable cervical spine pathologies that included; degenerative disease, trauma, neoplasms, metabolic-inflammatory disorders and congenital anomalies.</p> <p>Methods</p> <p>A retrospective review of total 785 lateral mass screws were placed in patients ages 16-68 years (40 females and 70 males). All cases were performed with a polyaxial screw-rod construct and screws were placed by using Anderson-Sekhon trajectory. Most patients had 12-14-mm length and 3.5 mm diameter screws placed for subaxial and 28-30 for C1 lateral mass. Screw location was assessed by post operative plain x-ray and computed tomography can (CT), besides that; the facet joint, nerve root foramen and foramen transversarium violation were also appraised.</p> <p>Results</p> <p>No patients experienced neural or vascular injury as a result of screw position. Only one patient needed screw repositioning. Six patients experienced superficial wound infection. Fifteen patients had pain around the shoulder of C5 distribution that subsided over the time. No patients developed screw pullouts or symptomatic adjacent segment disease within the period of follow up.</p> <p>Conclusion</p> <p>decompressive cervical spine laminectomy and Lateral mass screw stabilization is a technique that can be used for a variety of cervical spine pathologies with safety and efficiency.</p

Factors associated with good outcome using lateral mass plate fixation

The immediate and long-term outcomes of 70 consecutive patients who underwent subaxial lateral mass fixation between June 1996 and June 2001 were reviewed. Intraoperative fluoroscopy and somatosensory evoked potential (SEP) monitoring were used in all patients. Immediate postoperative computed tomography (CT) was performed to determine screw trajectory and placement. Follow-up ranged from 2 to 7 years. Postoperative CT showed 206 (58%) of 356 screws had unicorticate and 42% bicorticate purchase. Furthermore, 96 (27%) screws had suboptimal trajectory, but only 5 of these screws minimally penetrated the foramen transversarium without resultant vascular or neurological sequelae. A sudden unilateral intraoperative SEP amplitude decrease during screw placement in 2 patients resolved with screw removal and alteration of screw trajectory. The overall fusion success rate was 91.5% and screw pull-out developed in 2 patients. The recommended, drilling technique and trajectory (15degrees-25degrees rostral in the sagittal plane, 20degrees-30degrees lateral in the axial plane), supplemented bone grating, and intraoperative SEP monitoring are all associated with good screw placement, fusion, and neurological outcome and are recommended for all lateral mass fusion procedures

Toward Patient-Specific Prediction of Ablation Strategies for Atrial Fibrillation Using Deep Learning

Atrial fibrillation (AF) is a common cardiac arrhythmia that affects 1% of the population worldwide and is associated with high levels of morbidity and mortality. Catheter ablation (CA) has become one of the first line treatments for AF, but its success rates are suboptimal, especially in the case of persistent AF. Computational approaches have shown promise in predicting the CA strategy using simulations of atrial models, as well as applying deep learning to atrial images. We propose a novel approach that combines image-based computational modelling of the atria with deep learning classifiers trained on patient-specific atrial models, which can be used to assist in CA therapy selection. Therefore, we trained a deep convolutional neural network (CNN) using a combination of (i) 122 atrial tissue images obtained by unfolding patient LGE-MRI datasets, (ii) 157 additional synthetic images derived from the patient data to enhance the training dataset, and (iii) the outcomes of 558 CA simulations to terminate several AF scenarios in the corresponding image-based atrial models. Four CNN classifiers were trained on this patient-specific dataset balanced using several techniques to predict three common CA strategies from the patient atrial images: pulmonary vein isolation (PVI), rotor-based ablation (Rotor) and fibrosis-based ablation (Fibro). The training accuracy for these classifiers ranged from 96.22 to 97.69%, while the validation accuracy was from 78.68 to 86.50%. After training, the classifiers were applied to predict CA strategies for an unseen holdout test set of atrial images, and the results were compared to outcomes of the respective image-based simulations. The highest success rate was observed in the correct prediction of the Rotor and Fibro strategies (100%), whereas the PVI class was predicted in 33.33% of the cases. In conclusion, this study provides a proof-of-concept that deep neural networks can learn from patient-specific MRI datasets and image-derived models of AF, providing a novel technology to assist in tailoring CA therapy to a patient