Petroleum product transportation considered as one of the crucial parts of dangerous material transportation is a risky logistics activity. The selection of the appropriate tanker vehicles may be a suitable solution to reduce the risks and increase the efficiency and performance of the fuel transportation companies. However, the selection of a suitable road tanker vehicle is not an easy task for decision-makers as there are many conflicting criteria and many decision alternatives. In addition, decision-makers may have to decide with insufficient information since collecting crisp values may not be possible at all times. Hence, many ambiguities affecting the evaluation results exist in an assessment process performed to select the best tanker vehicle option. This paper suggests a novel integrated fuzzy approach to solve these decision-making problems. Sensitivity analysis is conducted to test the validation of the proposed integrated fuzzy approach and its results was performed by forming 130 scenarios. The results of sensitivity analysis prove that the proposed model can be applied to solve these kinds of decision-making problems.

The leaf area measurement is an important parameter in understanding the growth and physiology of a plant. Therefore, this study aimed to develop the best leaf area estimation model for tomato plants grown in plastic greenhouse conditions. The artificial neural network (ANN) and regression analysis techniques were used in the formation of a leaf area estimation model by using the leaf width and leaf length measurements determined by the linear measurement method. The plant material for the study consisted of 420 leaf samples of the Typhoon F1 tomato type grown in plastic greenhouse conditions. In the comparison of the created models according to both methods, the criteria of selecting low values for the root mean square error (RMSE), the mean absolute error (MAE), and the mean absolute percentage error (MAPE), and high value for the determination coefficient (R 2) were taken into account, and the best estimation models were determined. In the comparison made according to these criteria, it was concluded that the error values of the ANN model [R 2 = 0.96, RMSE = 3.30, MAE = 1.94, and MAPE = 0.05] were lower than those of the regression model [R 2 = 0.92, RMSE = 4.71, MAE = 3.31, and MAPE = 0.08], and that the ANN method provided a better fit to the actual values; therefore, the ANN model can be used as an alternative method in estimating the leaf area.

Bu çalışmada belirli bir konu üzerinde farklı araştırıcılar tarafından yapılmış olan yayınlanmış ya da yayınlanmamış birçok çalışmayı bir araya getirmek amacıyla kullanılan meta analizin tarımsal alanda kullanılabilirliği anlatılmıştır. Tarımsal alandan seçilen 8 farklı uygulama örneklerine ait veriler MSQL veri tabanına aktarılarak Comprehensive Meta Analysis istatistik paket programında analiz edilmiştir. Çalışmalarda etki ölçütü olarak odds oranı seçilmiş ve çalışmaların birleştirilmesi Mantel-Haenszel ve Peto yöntemlerine göre iki farklı şekilde gerçekleştirilmiştir. Meta analiz uygulama örnekleri için özet odds oranları her iki model varsayımına göre incelenmiş ve tüm örneklerde birbirini destekler nitelikte bulunmuştur (p<0.05).

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