Amaç: Bu çalışmanın amacı, Quantec LX ve Profile 29 döner NiTi enstrümanlar ve Nitiflex ve paslanmaz çelik el enstrümanlarının eğri kanallardaki etkinliğinin karşılaştırılmasıdır. Gereç ve Yöntem: Bu ...çalışmada 25-40° kurvatür açısına sahip 48 adet mandibular molar dişe ait mesial kanallar, her biri 12 kanal içerecek şekilde 4 gruba ayrıldı. Preparasyon grupları olarak Nitiflex ve H tipi el enstrümanları, Profile 29 ve Quantec LX döner NiTi enstrümanlar kullanıldı. Çalışmanın sonunda kanal eğiminde ve çalışma boyunda meydana gelen değişiklikler, alet kırıkları ve perforasyonlar kaydedildi. Verilerinin istatistiksel olarak değerlendirilmesinde Wilcoxon eşleştirilmiş iki örnek testi, Kruskal-Wallis ve Mann Whitney-U testleri kullanıldı. Bulgular: Döner NiTi preparasyon sistemlerinin kanal eğiminde ve çalışma boyunda paslanmaz çelik kanal eğelerine göre daha az değişime neden olduğu saptandı. Paslanmaz çelik ve Nitiflex kanal eğelerinde alet kırığı saptanmadı. Döner NiTi preparasyon sistemlerinde lateral perforasyonlar görülürken, paslanmaz çelik kanal eğesi kullanılan grupta ise strip perforasyonlar saptandı. Sonuçlar: Sonuç olarak, döner NiTi preparasyon sistemlerinin eğri kök kanallarını, elle kullanılan preparasyon sistemlerine göre daha etkili bir şekil
A new ensemble of features for breast cancer diagnosis Esener, I. Isikli; Ergin, S.; Yuksel, T.
2015 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)
Conference Proceeding
In this paper, an automatic Computer Aided Diagnosis (CAD) system is completely designed for breast cancer diagnosis and it is verified on a publicly available mammogram dataset constructed during ...Image Retrieval in Medical Applications (IRMA) project. This database comprises three different patch types indicating the health status of a person. These types are normal, benign cancer, and malignant cancer and they are labeled by the radiologists for the IRMA project. In the realization of CAD system, all mammogram patches are firstly preprocessed performing a histogram equalization followed by Non-Local Means (NLM) filtering. Then, the Local Configuration Pattern (LCP) algorithm is performed for feature extraction. Besides, some statistical and frequency-domain features are concatenated to LCP-based feature vectors. The obtained new feature ensemble is used with four well-known classifiers which are Fisher's Linear Discriminant Analysis (FLDA), Support Vector Machines (SVM), Decision Tree, and k-Nearest Neighbors (k-NN). A maximum of 94.67% recognition accuracy is attained utilizing the new feature ensemble whereas 90.60% was found if only LCP-based feature vectors are used. This consequence obviously reveals that the new feature ensemble is more representative than an LCP-based feature vector by itself.
The biocompatibility and apical microleakage of tricalcium phosphate based Sankin Apatite (SA) Type I, II, and III root canal sealers were investigated. Teflon tubes containing freshly mixed test ...materials were implanted in the subcutaneous tissue of mice. The observation periods were 24 h, 7, and 30 days, after which the areas of tissue reaction to the implanted materials were histopathologically analyzed. A dye-recovery, spectrophotometric method was used to evaluate apical microleakage. Results showed that the severity of tissue reaction among the tested materials decreased with time and at the end of the observation period both SA Type II and Type III were found more biocompatible than either Type I or Grossman's cement (GC). On the other hand, a fibrous tissue capsule was seen around the implants. There was no significant difference in spectrophotometrically measured leakage among teeth obturated with the test materials.
Load forecasting is the first phase of electric power system planning for economic power generation-distribution, effective control and operation conditions of the system, and also energy pricing. In ...this study, short-term load forecasting, as the main tool for economic operation conditions, is realized. 24-hour-ahead load forecasting without temperature data for Turkey is aimed and structures with ANN, Wavelet Transform & ANN, Wavelet Transform & RBF Neural Network, and EMD & RBF Neural Network are proposed for forecasting process. Local holidays' load data is replaced with normal day's characteristic to remove the disturbing effects of those days. To have more accurate forecast, a regulation to load forecast is proposed. Unregulated and regulated forecast error percentages of all days except local holidays are calculated as average daily MAPE and maximum MAPE. All MAPE values are compared between the proposed structures.
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