Purpose To evaluate the serum fibrinogen/albumin ratios in patients with acute and chronic central serous chorioretinopathy, and healthy control samples. Methods Serum fibrinogen/albumin ratios were assessed in patients with acute (Group-1, 30 eyes) and chronic (Group-2, 30 eyes) central serous chorioretinopathy, and compared with healthy control (Group-3, 30 eyes) samples. Results Fibrinogen/albumin ratios were significantly higher in Group-1 (104.72 ± 12.34) than in Group-2 (75.83 ± 10.06) and in Group-3 (72 ± 9.54) ( p = 0.001). No significant correlation was found between age, CMT, and BCVA with fibrinogen/albumin ratios in the Pearson correlation analysis. In the ROC curve analysis, the most appropriate cut-off value of the fibrinogen/albumin ratio for acute CSCR was ≥87.8 and the optimal cut-off value for the fibrinogen/albumin ratio for chronic CSCR was ≥68.6. Conclusion The fibrinogen/albumin ratio may be useful as an inflammatory biomarker to monitor the systemic inflammatory state during the treatment and follow-up in patients with acute CSCR.
This study analyses forest dynamics and land use/land cover change over a 43-year period using spatial-stand-type maps of temporal forest management plans of Karaisalı Forest Enterprise in the Eastern Mediterranean Region of Turkey. Stand parameters (tree species, crown closures and developmental stages) of the dynamics and changes caused by natural or artificial intervention were introduced and mapped in a Geographic Information System (GIS) and subjected to fragmentation analysis using FRAGSTATS. The Karaisalı Forest Enterprise was first planned in 1969 and then the study area was planned under the Mediterranean Forest Use project in 1991 and five-term forest management plans were made. In this study, we analysed only four periods (excluding 1982 revision plans): 1969, 1991, 2002 and 2012. Between 1969 and 2012, overall changes included a net increase of 3,026 ha in forested areas. Cumulative forest improvement accounted for 2.12% and the annual rate of total forest improvement averaged 0.08%. In addition, productive forest areas increased from 36,174 to 70,205 ha between 1969 and 2012. This translates into an average annual productive forest improvement rate of 1.54%. At the same time, fully covered forest areas with crown closure of "3" (>70%) increased about 21,321 ha, and young forest areas in developmental stage of "a" (diameter at breast height (dbh) < 8 cm) increased from 716 to 13,305 ha over the 43-year study period. Overall changes show that productive and fully covered forest areas have increased egregiously with a focus on regenerated and young developmental stages. A spatial analysis of metrics over the 43-year study period indicated a more fragmented landscape resulting in a susceptible forest to harsh disturbances.
ÖzetBu çalışmanın amacı, Landsat 8 uydu görüntüsü kullanarak arazi kullanım sınıflarını farklı kontrollü sınıflandırma algoritmaları ile sınıflandırmak ve en uygun tekniği ortaya koymaktır. Bu amaçla, en yüksek olasılık (maksimum likelihood) ile destek vektör makineleri (DVM) için yaygın bir şekilde kullanılan doğrusal, polinom, radyal ve sigmoid kernel fonksiyonları kullanılmıştır. En iyi sonucu veren en yüksek olasılık metodu ile DVM polinom fonksiyonu çıktıları karşılaştırılmıştır. En yüksek olasılık metodu için kappa değeri ve genel sınıflandırma başarısı sırasıyla 0.81 ve %85'dir. DVM polinom fonksiyonu için ise bu değerler sırasıyla 0.79 ve %84'tür. Ayrıca, her iki yöntemle sınıflandırılmış arazi kullanım sınıflarının konumsal analizi Coğrafi Bilgi Sistemleri kullanılarak yapılmıştır. Konumsal analiz sonuçlarına göre en yüksek olasılık metodu kullanılarak toplam alanın %47.5'i, DVM polinom fonksiyonu ile %43.3'ü doğru bir şekilde sınıflandırılmıştır.Anahtar Kelimeler: Arazi kullanım sınıfları, En yüksek olasılık, Destek vektör makineleri, Landsat 8 uydu görüntüsü
Comparison of Different Supervised Classification Algorithms for Land Use Classes AbstractThe aim of this study was to classify land use classes using Landsat 8 satellite image with different supervised classification algorithms and demonstrate the most proper technique. For this purpose, the highest probability (maximum likelihood) classification method and linear, polynomial, radial and sigmoid kernel functions for support vector machines (SVM) were used. The SVM method polynomial function and the maximum likelihood method which give better results were compared. The result showed that the maximum likelihood method was estimated with a 0.81 kappa statistic and 85% overall accuracy assessments, respectively. The SVM polynomial function for these values was 0.79 and 84%. Spatial analysis of land use classes that were classified using both methods was also made by Geographical Information System. According to the spatial accuracy assessment results, 47.5% and 43.3% of total area were classified accurately by the maximum likelihood method and the SVM method, respectively.
This study introduces the artificial neural networks (ANN) function to model relationship between diameter at breast height (dbh) and stump diameter and investigates the potential of ANN model to obtain the prediction of dbh. In total, 583 diameters at breast heightstump diameter pairs were measured in 61 plots sampled from Crimean pine [Pinus nigra subsp. pallasiana] stands in ÇAKÜ Research Forest, Çankırı, Turkey. The network models, including the activation functions of function between input layer and hidden layer and pure-lin function between hidden layer and output layer (A6 alternative) with 12 # neurons, were found to the better predictive with lower error values including SSE (2585.3869), AIC (821.5731), BIC (825.7817), RMSE (2.2831), MSE (5.2125) and higher fitting value, such as R 2 adj (0.9372), than those of other prediction methods. The best predictive ANN model resulted in the reductions of SSE, AIC, BIC, RMSE and MSE by 9.8486 %, 5.9018 %, 5.8735 %, 5.0519 % and 9.8486 %, and R 2 adj in the increase of 0.7377 % as compared to those by the regression model. This present study has underlined the capability of the ANN model for predicting the relationship between dbh and stump diameter. This novel artificial intelligence technique provides a modeling alternative for forest managers to predict dbh required information for the management of forests.
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