Debris removal during disaster response phase : a case for Turkey

Ankara : The Department of Industrial Engineering and the Graduate School of Engineering and Science of Bilkent University, 2013.Thesis (Master's) -- Bilkent University, 2013.Includes bibliographical references leaves 88-93.In this study, a methodology to provide emergency relief supplies to the disaster affected regions is developed. As a result of destructive effects of disasters, debris, which is the ruin and wreckage of the structures, occurs. Proper removal of debris has significant importance since it blocks the roads and prohibits emergency aid teams to access the disaster affected regions. Wrong disaster management, lack of efficiency and delays in debris removal cause disruptions in providing sheltering, nutrition, healthcare and communication services to the disaster victims, and more importantly they result in loss of lives. Due to the importance of a systematic and efficient way of debris removal from the point of improving disaster victims’ life quality and its contributions to transportation of emergency relief materials to the disaster affected regions, the focus of this study is providing emergency relief supplies to the disaster affected regions as soon as possible, by considering unblocking operations of roads through removing the accumulated debris. To come up with a scientific solution methodology to the problem, mathematical models that select the paths in order to transport emergency aid materials in the presence of debris to the pre-determined disaster affected regions are developed. The performances of the models are tested on two distinct data sets from İstanbul. Since it is crucial to act quickly in an emergency case, a constructive and an improvement heuristic are also proposed.Şahin, HalenurM.S

Tıbbi teşhis problemleri için UTADIS temelli çok amaçlı evrimsel algoritmalar.

We develop hybrid methods that integrate multi-criteria decision making, evolutionary algorithms and machine learning to be used in medical diagnosis problems. The proposed models classify patients into two categories according to their disease status with the aim of obtaining high classification performances both classes under consideration. First, we develop a Mixed-Integer Linear Programming approach, Parametrized Classification Model (PCM), which is based on UTADIS. By solving PCM multiple times with various values of a specific parameter, we obtain a set of solutions spread over the Pareto-optimal front in the space of true positive and true negative responses. Then, to combine strong aspects of these solutions, we integrate PCM with evolutionary algorithms, NSGA-II and RECGA, to tune the classification parameters acquired by PCM. NSGA-II favors non-dominated solutions in terms of sensitivity and specificity and RECGA aims to perform well particularly in situations where the incidence of the disease may be relatively low, such as general screening. We call the developed integrated models as PCM+NSGA-II and PCM+RECGA, respectively. In order to observe the model performances, we try them with three different datasets which are about coronary stent patients and breast cancer. Furthermore, we apply several well-known machine learning algorithms to these datasets and compare the results with the results of PCM+NSGA-II and PCM+RECGA. Additionally, for the coronary stent dataset, the model performances are compared with those of cardiologists. The results indicate that PCM+NSGA-II and PCM+RECGA are promising classification algorithms that can be used in medical decision support tools by medical experts.Thesis (Ph.D.) -- Graduate School of Natural and Applied Sciences. Industrial Engineering

Uterine Cervix Metastasis of Myxopapillary Ependymoma Originated from the Spinal Cord

Background: Myxopapillary ependymomas are well differentiated low-grade tumors which have been documented to local or distant metastasis. In the literature, this is a unique case of myxopapillary ependymoma with metastasis to the uterine cervix. Here, we present a rare case of extra neural metastasis of spinal ependymoma that developed over a long period. Case Report: A 34-year-old woman was referred to our hospital for pelvic mass. A mass (110x100 mm) localized between the sacrococcygeal region and the uterus was detected by magnetic resonance imaging. In 2004, she had been operated upon for myxopapillary ependymoma seated in the sacrococcygeal region for the first time. She underwent tumor resection eight times due to the recurrence of spinal tumor in the same region in nine years. Under the diagnosis of uterine neoplasm, we carried out radical hysterectomy, omentectomy and pelvic lymphadenectomy as the surgical procedure. The pathological findings were reported as myxopapillary ependymoma. Immunohistochemically, the myxopapillary ependymal cells showed strong positivity for glial fibrillary acidic protein, whereas they were negative for low molecular weight cytokeratin. The Ki-67 labeling index was about 2-3%. The patient had an uneventful postoperative period. She has remained free of symptoms in the year since surgery. Conclusion: Extra-spinal myxopapillary ependymoma is very rare, but it must be considered in the differential diagnosis of pelvic mass lesions