Clean and sustainable environment problems in forested areas related to recreational activities: case of Lithuania and Turkey.

Introduction: With the acceleration of social life, people's interest and demand for forestry recreation activities is increasing. However, with this increase, it is inevitable that negative environmental effects will occur. Particularly mass participation poses an important risk for environmental sustainability. In this context, the aim of this study is to determine the recreational activities organized in forest areas in Turkey and Lithuania, the environmental effects of these activities and the precautions to be taken. Methods: In Turkey and Lithuania, interviews were conducted to determine the attitudes of experts involved in recreational activity processes towards a clean environment and environmental sustainability. A semi-structured interview form was used in the interviews with forest operators and other experts. The sample group of the research consists of 17 experts from Turkey and Lithuania. Results: According to the results of the research, recreational activities are organized in forest areas in both countries, but the most important problem related to these activities is waste production. In addition, there is also damage to the natural environment. Although there are legal regulations in both countries, there are no definite results in solving environmental problems. Conclusions: It can be said that necessary measures such as raising awareness of people and ecological education should be taken in order to ensure the right of individuals to live in a safe and clean environment and at the same time to ensure sustainability in forest areas. as the improvement of legal regulation.

Artificial Neural Network Approach for Modeling of Effect of Ultrasound on the Dissolution of Magnesia in Aqueous Carbon Dioxide.

This article is about dissolving magnesia in aqueous carbon dioxide by applying ultra sound. Particle size, reaction temperature, and solid/liquid ratio were chosen as the experimental parameters. As a result of the experimental study, the ultrasound energy conversion fraction (USECF) was obtained. Using experimental data, a model has been created for artificial neural networks and USECF. Created and modeled, the particle size, time, reaction temperature, solid/liquid ratio, and amplitude rate were determined as input variables. USECF was determined as the output variable of the model. In this study, six different ANN models were created by using two different learning algorithms and three different transfer functions. The results of these models were compared with the experimental results. It has been determined that the model established with the Levenberg Marquart learning algorithm and the TANSIG transfer function gives the best result of the ANN model compared to the other models. The ANN model established with the Gradient Descent learning algorithm and the LOGSIG transfer function were determined to be the second model that gave the best results. The regression R value for the model performance indicator training data was determined as 0.99 after validation, and the regression R value for the test data was determined as 0.99.

Speed violation analysis of heavy vehicles on highways using spatial analysis and machine learning algorithms.

With the development of technology in the world, vehicles that reach high speeds are produced. In addition, with the increase of road width and quality, faster and more comfortable transportation can be provided. These developments also increase the speed violation rates of road vehicles. Drivers who violate speed limits can endanger both their own lives and the lives of others. Speed violations, of especially heavy vehicles, involve much greater risks than that of light vehicles. Heavy vehicles can cause more serious losses of lives and property in accidents, compared to the ones caused by light vehicles, as they can carry much more freight or passengers than light vehicles. In this study, data regarding the speed violations committed by heavy vehicles in Turkey, were used. Speed violations were divided into 10 classes according to the intensity of speed violation rates. After this process, all provinces were classified according to support vector machines (SVM), naive bayes (NB) and k-nearest neighbors (KNN) algorithms. When the accuracy values and error scales of all three algorithms are examined, it has been determined that the algorithm that gives the most accurate results is the NB algorithm. Based on the classification of this algorithm, speed violation density maps of types of heavy vehicles in Turkey were created by using spatial analysis. According to the density maps, the provinces with the highest speed violations were identified. In the results, it was determined that the rate of heavy vehicle speed violation was highest in the cities such as Erzurum, Konya, and Mugla. Later, these cities were examined in terms of heavy vehicle mobility. At the end of this study, measures were proposed to reduce these violations in cities where speeding violations are intense. Material and moral damages can be prevented, to a great extent, with the implementation of recommendations of policymakers which can reduce speed violations.

FX minirail balloon angioplasty for the treatment of patients with in-stent restenosis: a matched comparison of 6 months' outcome with conventional balloon angioplasty.

The optimal treatment for in-stent restenosis (ISR) is a subject of controversy. Recently, FX minirail balloon angioplasty (BA) has emerged as a management tool for ISR. We assessed the hypothesis that the FX minirail BA has advantages over conventional percutaneous transluminal balloon angioplasty (PTCA) in the treatment of ISR. FX minirail BA or PTCA were applied to 116 patients with ISR (145 lesions) at our institution. Using a computer algorithm, an attempt was made to match each lesion in the FX minirail BA group with a corresponding lesion in the PTCA group. The lesion pairs should match with respect to the patients' age and sex, type of target vessel and stent, reference vessel diameter, and baseline minimal lumen diameter (MLD). Following the matching process, 46 ISR lesion pairs were identified. Baseline patient characteristics were similar among the groups (P=not significant). There was no difference in the in-hospital major adverse cardiac events (MACE) between the groups, whereas MACE at follow-up were significantly lower in the FX minirail BA group than in the PTCA group (19.6% vs 41.3%, P <0.05). The recurrent ISR rate was significantly lower in the FX minirail BA group than in the PTCA group (19.6% vs 39.1%, P <0.05). Also, a diffuse pattern of recurrence was more common in lesions treated with PTCA, whereas the focal pattern of recurrence was more common in the FX minirail BA group (22.2% vs 50%, P <0.05). The MLD at follow-up, the acute gain, and net gain were significantly higher in the group of lesions treated with the FX minirail BA than in the PTCA group. In addition, a significantly higher late loss and loss index at follow-up were observed in the PTCA group compared in the FX minirail BA group. The FX minirail BA has advantages over PTCA in the treatment of patients with ISR, with better immediate and follow-up angiographic outcomes, and a better clinical outcome on follow-up.

Transient partial amnesia complicating cardiac and peripheral arteriography with nonionic contrast medium.

The aim of this study was to present a case of disruption of the blood-brain barrier during the coronary and lower extremity angiographies with radiological and clinical findings. This condition was secondary to intraarterial use of a nonionic, monomeric contrast medium. A total of 450 cc contrast media was used. Computed tomography examination showed contrast enhancement of the right occipital and frontoparietal cortical regions, which returned to normal one day after. The patient also fully recovered from the neurological symptoms within 24 h. We discussed the possible mechanism for blood-brain barrier disruption in this case.