This study presents a new sex pheromone trap for European Grapevine Moth, Lobesia botrana, to monitor its field population without direct field observations in Turkey. The study was conducted in Hadim and Taşkent (Konya) districts in 2016. Viticulture has been carried out for many years in both districts where the maximum vineyards are located. Due to the distance to the center and the difficulty of transportation to these districts, there has not been conducted any detailed studies until today. Therefore, this study about L. botrana, which is harmful in these vineyards, was planned. In order to monitor the pest population, the cameras were placed onto the pheromone traps, and it was tried to get information about the population development of the pest via internet without going to the vineyards. For this purpose, four locations in Hadim and one location in Taşkent district were selected and one pheromone trap was established in each location before the bud burst in vineyards. The camera was placed on each pheromone trap and was linked with vMEyeIPC program via internet. The time of first adult flight and the population development of adult L. botrana were determined. The first adult flight began at the beginning (7/4/2016) and in the middle of April (15/4/2016) in Hadim and Taşkent, respectively. The number of offspring and the maximum number of pest individuals caught in traps were recorded in vineyards in both districts.

International Journal of Electrical Energy (IJOEE)

Histopathologic images are time consuming for both specialist and machine learning methods with their complex structure and huge dimensions. In these cases, delays in the diagnosis of disease occur, as well as the treatment of fewer patients. When the histopathological images are examined at low resolution for shortening the examination time, it is almost impossible to identify the cancerous regions. When examining high-resolution images, it takes a long time to inspect because the image is divided into patches. Despite the fact that fairly fast machine learning methods are offered for the shortening of the analysis period, the number of patches to be examined has a negative effect on the decision time. For this reason, the area under examination needs to be reduced. For this, first of all, the destruction of cell-free areas and then the destruction of areas containing noncancerous cells must be eliminated. An effective and fast method of area reduction is presented for faster analysis and real-time use of histopathological images by machine learning algorithms. A two-step approach is used in the proposed method. In the first step, 3 × 3 texture properties of images are obtained and discrete wavelet transform is applied. Then, the image is cleaned with simple morphological processes. In the second step, the cleaned image is subjected to a discrete wavelet transform. Thus, the changes in cell-containing regions are captured, and regions that may be dangerous are identified. The proposed method reduced the areas to be examined by 98.5% to 99.5% with 95.33% accuracy.

Sleep electroencephalogram (EEG) signal is an important clinical tool for automatic sleep staging process. Sleep EEG signal is effected by artifacts and other biological signal sources, such as electrooculogram (EOG) and electromyogram (EMG), and since it is effected, its clinical utility reduces. Therefore, eliminating EOG artifacts from sleep EEG signal is a major challenge for automatic sleep staging. We have studied the effects of EOG signals on sleep EEG and tried to remove them from the EEG signals by using regression method. The EEG and EOG recordings of seven subjects were obtained from the Sleep Research Laboratory of Meram Medicine Faculty of Necmettin Erbakan University. A dataset consisting of 58 h and 6941 epochs was used in the research. Then, in order to see the consequences of this process, we classified pure sleep EEG and artifact-eliminated EEG signals with artificial neural networks (ANN). The results showed that elimination of EOG artifacts raised the classification accuracy on each subject at a range of 1%– 1.5%. However, this increase was obtained for a single parameter. This can be regarded as an important improvement if the whole system is considered. However, different artifact elimination strategies combined with different classification methods for another sleep EEG artifact may give higher accuracy differences between original and purified signals.

As population increases, one of the factors affecting life is traffic. Efficient traffic management has a direct positive impact on issues such as time, carbon dioxide emissions, and fuel consumption. Today, an important parameter under the heading of traffic is the signalling systems for intersections, which are operated with fixed-time, semi-actuated, fully actuated, and fully adaptive control methods. In this study, an adaptive traffic management model is developed for signalised intersections. The adaptive traffic management model developed includes phase extension with minimum and maximum time intervals dependent on density and phase skip features. Additionally, the most distinctive feature of the model is its flexible phase structure rather than a sequential phase. The Heybe intersection, located within the boundaries of Antalya province, is modelled one-to-one in the simulation of urban mobility (SUMO) simulation programme with real intersection data. The developed adaptive traffic management model is applied to the Heybe intersection, and the effects of the model are revealed. Improvements obtained from the SUMO simulation programme were verified through visual inspection, and high-accuracy results were determined. As a result of the studies, it was found that the application of the adaptive traffic management model developed at Heybe intersection, which has approximately 50,000 vehicles passing daily, resulted in a 27.2 % improvement in the average delay per vehicle parameter, a 32.4 % improvement in the average waiting time per vehicle parameter, and a 16.7 % improvement in the average speed per vehicle parameter.