Objective: Spinal cord ependymomas account for 3–6% of all central nervous system tumors and around 60% of all intramedullary tumors. The aim of this study was to analyze the neurological outcome after surgery and to determine prognostic factors for functional outcome. Patients and Methods: Patients treated surgically due to a spinal cord ependymoma between 1990 and 2018 were retrospectively included. Demographics, neurological symptoms, radiological parameters, histopathology, and neurological outcome (using McCormick Score [MCS]) were analyzed. Possible prognostic factors for neurological outcome were evaluated. Results: In total, 148 patients were included (76 males, 51.4%). The mean age was 46.7 ± 15.3 years. The median follow-up period was 6.8 ± 5.4 years. The prevalence was mostly in the lumbar spine (45.9%), followed by the thoracic spine (28.4%) and cervical spine (25.7%). Gross-total resection was achieved in 129 patients (87.2%). The recurrence rate was 8.1% and depended on the extent of tumor resection ( p = 0.001). Postoperative temporary neurological deterioration was observed in 63.2% of patients with ependymomas of the cervical spine, 50.0% of patients with ependymomas of the thoracic spine, and 7.4% of patients with ependymomas of the lumbosacral region. MCS 1–2 was detected in nearly two-thirds of patients with cervical and thoracic spinal cord ependymoma 36 months after surgery. Neurological recovery was superior in thoracic spine ependymomas compared with cervical spine ependymomas. Poor preoperative functional condition (MCS >2), cervical and thoracic spine location, and tumor extension >2 vertebrae were independent predictors of poor neurological outcome. Conclusion: Neurological deterioration was seen in the majority of cervical and thoracic spine ependymomas. Postoperative improvement was less in thoracic cervical spine ependymomas compared with thoracic spine ependymomas. Poor preoperative status and especially tumor extension >2 vertebrae are predictors of poor neurological outcome (MCS >2).

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Modeling of unmanned aerial vehicle (UAV) with system identification is very important in terms of its model-based effective control. The modeling of UAV is required for aircraft crashes, analyzing autonomous aircrafts, preventing external disturbances, pre-flight analysis. However, since UAV has nonlinear inherent dynamics including inherent chaoticity and fractality, it becomes difficult to obtain a mathematical model under external disturbance. In this study, some of the inherent nonlinear dynamics of UAV are linearized and the model of UAV is obtained by system identification approaches under external disturbance. The linearized lateral dynamics of a fixed wing UAV is used in this study. Further, the flight motion equations applied to fixed wing UAV have been utilized for obtaining the coefficients of lateral model for straight and level flight. The roll angles are calculated using transfer functions for aileron, rudder and deflections inputs. The autoregressive exogenous (ARX), autoregressive moving average with exogenous (ARMAX) and output error (OE) parametric system identification approaches are performed to estimate UAV lateral dynamic system response as using empirical input-output data sets. The accuracy of parametric model estimation and model degrees are compared for different external disturbance effects.