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Recreational Exercise Motives of Adolescents and Young AdultsThis study examined the motives for taking part in recreational exercise/sporting activities based on a sample of Turkish young people. Secondly, gender and age group differences with motivational dimensions were investigated. A total of 298 members of 6 Youth Centers voluntarily participated in this study. The Recreational Exercise Motivation Measure (REMM) was administered to all participants. Two × two MANOVA revealed significant main effect for gender (p < 0.03) and age group (p < 0.01). Univariate follow-up tests revealed that the significant main effect for gender was attributable to health subscale, and age group differences were correlated with health, body/appearance, social/enjoyment, and skill development subscales (p < 0.01). It can be concluded that health motives were more important for females than males, and motives relating to health, appearance, and social/enjoyment were more important for young adults.

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Objective:To report our experience with Fournier’s Gangrene (FG) over the past eight years and evaluate the predisposing factors which affect the mortality.Methods:Sixty-five patients who were admitted to emergency surgical unit of our institution presenting with FG between January 2006 and August 2014 were included. The anatomical site of infective gangrene, predisposing factors, etiological factors, and outcomes were retrospectively reviewed.Results:Our cases included 8 women and 57 men. The average age of men was 51±13.9 (range 19-75) and the average age of women was 63±10.5 (range 52-76). Average hospitalization time was 9.2±6.6 days (range 5-25) days. The most frequent comorbid disease was diabetes mellitus and the most frequent etiology was perianal abscess. Colostomy was performed in 11 patients, orchidectomy in two patients, cystostomy in two patients. Notably, all of the 8 (12.3%) patients who died from FG had diabetes and low socioeconomic status. A total of six patients who died required more than one surgical debridement.Conclusions:Fournier’s gangrene is a severe surgical emergency, with a high mortality rate. Low socioeconomic status, diabetes and more than one debridement play a major role in mortality and morbidity.

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Environment of the conscious of puclic opinion is increase recent years. When making use of the forest ecosystem for various purposes, care must be taken not to spoil the forest structure. To achieve this, forests must be used according to forestry techniques. Forest roads planning standarts (slope, width, pavement, drainage etc.) are different in production forests, forestation, national park and protection forests.The need for total forest roads in Turkey was 201810 km and that 133693 km portion of which, corresponding to 66.25%, was constructed by the end of 2004. With a view to be capable of conducting modern forestry activities, our country's forests should be provided with a systematic roads network by completing as soon as possible the construction of the remaining portion of such roads. This study gives functional planning criterion of forest road network system in Turkey. Road spacing and road density has been inspected concerning about forest roads in production forests, reforestation, national park and protection forests in Turkey

Early diagnosis of COVID-19, the new coronavirus disease, is considered important for the treatment and control of this disease. The diagnosis of COVID-19 is based on two basic approaches of laboratory and chest radiography, and there has been a significant increase in studies performed in recent months by using chest computed tomography (CT) scans and artificial intelligence techniques. Classification of patient CT scans results in a serious loss of radiology professionals' valuable time. Considering the rapid increase in COVID-19 infections, in order to automate the analysis of CT scans and minimize this loss of time, in this paper a new method is proposed using BO (BO)-based MobilNetv2, ResNet-50 models, SVM and kNN machine learning algorithms. In this method, an accuracy of 99.37% was achieved with an average precision of 99.38%, 99.36% recall and 99.37% F-score on datasets containing COVID and non-COVID classes. When we examine the performance results of the proposed method, it is predicted that it can be used as a decision support mechanism with high classification success for the diagnosis of COVID-19 with CT scans.

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