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This study presents the use of prior knowledge of inverse artificial neural network (ANN) to model and optimize a reconfigurable N-shaped microstrip antenna. Three accurate prior knowledge inverse ANNs with large amount training data are proposed where the frequency information is incorporated into the structure of ANN. The complexity of the input/output relationship is reduced by using prior knowledge. Three separate methods of incorporating knowledge in the second step of the training process with a multilayer perceptron (MLP) in the first step are demonstrated and their results are compared to EM simulation.

Patent ductus arteriosus (PDA) is an important problem in premature infants. Extremely low birth weight infants (ELBWI) are so fragile with respect to surgical stress that minimally invasive procedures are required. We report 26 ELBWI cases with PDA who underwent surgical closure. All had failed indomethacin treatment, or it had been contraindicated. The mean gestational age at birth was 27 weeks (range, 24-38 weeks), and the mean birth weight was 960.96 g (range, 710-1440 g). The mean age at operation was 18.06 days (range, 7-34 days), and the mean body weight at operation was 989.42 g (range, 680-1460 g). There was no surgery-related mortality or morbidity. Our surgical procedures consisted of posterior muscle-sparing thoracotomy, clipping the PDA and no ligation, and closing the thorax without a tube thoracostomy. Muscle-sparing thoracotomy reduces the likelihood of long-term physical impairment and deformity, the clipping technique minimizes the dissection of surrounding PDA tissue, and the thorax is closed without a tube. Nursing care is simplified, costs are reduced, and the number of chest x-rays needed postoperatively is reduced. We believe that surgical closure of PDA without chest tube drainage can be accomplished safely in premature infants.

WOS: 000334148500010, PubMed: 24807474, Purpose: Endovenous laser ablation (EVLA) for superficial venous insufficiency is traditionally performed under tumescent local anesthesia as day case surgery. The aim of this study is to evaluate the feasibility of general anesthesia in addition to tumescent anesthesia in patients undergoing EVLA. Methods: The anesthesia and clinical registration records of 341 extremities of 300 adult patients were reviewed and analyzed retrospectively. Demographic and clinical data, preoperative anesthetic evaluation data (ASA physical status, preoperative airway assessment, Mallampati score), type of supraglottic device, duration of anesthesia and surgery, any surgical and/or anesthetic complication, timing of mobilization and discharge, and postoperative course were evaluated. Results: Mean duration of operation and anesthesia was 28 (12-55) and 40 (20-65) minutes, respectively. Mobilization and discharge timing was 25 (11-45) and 139 (110-200) minutes, respectively. All patients were discharged the same day of surgery. Conclusion: The combination technique of administering general anesthesia with supraglottic device and tumescent anesthesia is a safe and effective method to reduce the patients' pain and discomfort during the EVLT procedure within the scope of day case surgery.

Cardiac sarcomas are rare malignant tumors. Angiosarcoma is the most common cardiac sarcoma and is present in up to 33% of cases. Angiosarcomas have a poor prognosis, with a short survival expectancy. We report a case of a right atrial angiosarcoma treated by partial tumor resection followed by chemotherapy.

Artificial Neural Network (ANN) is an important technique for modeling and optimization in engineering design. It is very suitable in modeling as it needs only the information based on relationship between the input and the output related to the problem. For further improvement in modeling, a priori knowledge about the problem such as an empirical formula, an equivalent circuit model, and a semi-analytical equation is directly embedded in ANN structure through a knowledge based modeling strategy. Three-step modeling strategy that exploits knowledge based techniques is developed to improve some properties of conventional ANN modeling such as accuracy and data requirement. All these improvements ensure better accuracy with less time consumption compared to conventional ANN modeling. The necessary knowledge in this strategy is generated in the first step through conventional ANN. Then this knowledge is embedded in the new ANN model for the second step. Final model is constructed by incorporating the existing knowledge obtained by the second step. Therefore each model generates better accuracy than previous model. Conventional ANN, prior knowledge input, and prior knowledge input with difference techniques are used to improve accuracy, time consumption, and data requirement of the modeling in three-step modeling strategy. The efficiency of three-step modeling strategy is demonstrated on the nonlinear function modeling and the high dimensional shape reconstruction problem.

In space mapping, a time-consuming but accurate fine model is used along with a less accurate but fast coarse model to reduce the overall computational effort. In this work, techniques using the difference mapping concept will be introduced. These techniques are efficient in reducing the computational effort while improving convergence. Difference mapping is constructed similarly to the mechanism used in space mapping, but, unlike space mapping, it facilitates the use of terminating conditions based on the simultaneous use of input and output values. Rigorous mathematical expressions related to difference mapping techniques will be given, and the improvement provided by these techniques will be discussed. Furthermore, to expose the efficiency of using the difference in input and output, simulation results obtained for high-dimensional applications will be given.

Summary Artificial neural network (ANN) provides an efficient modeling technique based on input–output data obtained from an engineering problem. Highly nonlinear and complex relationships can be formed by ANN because of its nonlinear nature and representing knowledge at interconnection weights. If an ANN model is not sufficient in respect to the accuracy and time consumption, the knowledge based on design experience can be embedded into the modeling process. This knowledge reduces the complexity of the nonlinear input–output relationships; therefore, the knowledge embedded ANN can be formed easily compared with the conventional ANN model. Three-step modeling strategy generates the initial knowledge via the conventional ANN modeling and consists of three sequential and also discrete training processes exploiting the knowledge-based methods such as prior knowledge input and prior knowledge input with difference. The latter step improves the accuracy of the former step, and three-step modeling provides more accuracy than conventional ANN modeling. The efficiency of three-step modeling strategy is demonstrated on two data sets, which are obtained by the reconfigurable microstrip patch antenna design problem. The different number of neurons and ANN structures is handled for the comparison as well. In addition, ANN modeling is formed by MATLAB m-file through neural network toolbox to reveal the efficiency of knowledge-based three-step modeling strategy. Copyright © 2016 John Wiley & Sons, Ltd.

Primary spontaneous coronary artery dissection is one of the rare causes of acute myocardial infarction and is mostly fatal. Previous studies report that it is mostly seen in middle-aged women in the last trimester of pregnancy and early postpartum period. Clinical presentation of the disease is variable in pattern, and its severity is related to extent and development rate of dissection. Herein we present three cases of primary spontaneous left main coronary artery dissection. Two of the patients are men and the third patient is a non-pregnant 69-year-old woman. The cases were presented and discussed with review of the pertinent literature.

The surrogate methods have been used to ease the computational burden in various disciplines. In this work, a surrogate method based on space mapping is proposed to solve inverse problems. Even though the efficiency of space mapping and its variants has been demonstrated in numerous work, using it for inverse problems is addressed for the first time in this work. The efficiency of the proposed method is demonstrated solving the shape reconstruction of a conducting cylinder.

12th Congress of the European-Association-for-Clinical-Pharmacology-and-Therapeutics -- JUN 27-30, 2015 -- Madrid, SPAIN, WOS: 000384951200256, …, European Assoc Clin Pharmacol & Therapeut

Zellweger Syndrome is a rare autosomal recessive inherited disorder within the spectrum of peroxisome biogenesis disorders. It is a progressive and fatal disorder with multiple congenital anomalies. There may be some challenges for anesthesiologists in patients with Zellweger Syndrome. We report the anesthetic management of an infant with Zellweger Syndrome undergoing closure of patent ductus arteriosus and pulmonary artery banding.

Artificial neural networks have been used as an important technique in microwave modelling and optimisation. This paper gives an overview and recent developments on the knowledge-based neural modelling techniques in microwave modelling and design. The knowledge-based artificial neural networks are constructed by incorporating the existing knowledge such as empirical formulas, equivalent circuit models and semi-analytical equations in neural network structures. When one of the knowledge-based methods can not provide sufficient accuracy, two of them can be used in the same modelling process. This combination of methods is named hybrid technique. Using knowledge-based techniques requires less training data and has better extrapolation performance than classical neural networks. The advantages of using knowledge-based neural network modelling are demonstrated with microwave device modelling applications.

24th Signal Processing and Communication Application Conference (SIU) -- MAY 16-19, 2016 -- Zonguldak, TURKEY WOS: 000391250900166 Due to hardware limitations, hyperspectral imagery has low spatial resolution. It can be obtained super-resolution hyperspectral imagery by means of sparse representation-based methods that are designed for improving spatial resolution. In this paper, the effect of sparse representation-based dictionary learning algorithms including K-SVD, ODL and Bayes on obtaining superresolution images with low error and high quality has been investigated. The method with best results has been identified. IEEE, Bulent Ecevit Univ, Dept Elect & Elect Engn, Bulent Ecevit Univ, Dept Biomed Engn, Bulent Ecevit Univ, Dept Comp Engn