This study aimed to model the performance indices of deep bed drying of rough rice using artificial neural networks (ANNs), compare the ANN approach to the multivariate regression method, and determine the sensitivity of the ANN model to the input variables. The effects of air temperature, air velocity, and air relative humidity on drying kinetics, product output rate (POR), evaporation rate (ER), and percentage of kernel cracking (KC) were investigated. To predict the dependent parameters, 3 well-known networks, namely the multilayer perceptron, generalized feed forward (GFF), and modular neural network, were examined. The GFF networks with the Levenberg-Marquardt learning algorithm, hyperbolic tangent activation function, and 4-15-1, 3-4-4-1, 3-7-1, and 3-11-1 topologies provided superior results, respectively, for predicting moisture content, POR, ER, and CK. The values of all of the drying indices predicted by the ANN were closer to the experimental data than linear and logarithmic regression models. The output variables were significantly affected by the dependent variables. However, air temperature and air relative humidity showed the maximum and the minimum influence on the network outputs, respectively.

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This increase may also, at least partially, be a result of the global increase in mean environmental temperatures (Root et al., 2003). The recent expansion of wild boar populations in Europe has stimulated studies of their genetic diversity and structure in order to develop adequate management strategies (Veličković et al., 2014). The increase of wild boar populations has been registered in hunting areas and rural areas in Turkish Thrace and Anatolia, as well. This has led to substantial damage to agricultural crops and forestry, and even problems in urban habitats. As a result, effective management strategies for wild boars are necessary, as this species is considered to be a pest in many parts of the world (Bieber and Ruf, 2005; Scandura et al., 2011; Hajji and Zachos, 2011). Corbet (1978) stated that many races of Sus scrofa in the Palearctic were named based on differences in size and color; however, these subpopulations were never shown to have discrete boundaries. To date, 16 different subspecies have been recognized worldwide, including four geographical groups (Western, Indian, Eastern, and

Wing shape analysis on some species of Terellia serratulae (L., 1758) group (Diptera: Tephritidae) based on geometric morphometric analysis Bazı Terellia serratulae (L., 1758) grup (Diptera: Tephritidae) türlerinde geometrik morfometrik analiz temelli kanat şekil analizi

In this study, we investigated the genetic relationship and population differentiation within and among Apis florea populations sampled from three states in Iran, by using RAPD-PCR analysis. A total of 158 A. florea colonies from nine locations belonging to Ilam, Khuzestan and Bushehr states of Iran were evaluated. Of the 25 RAPD primers tested, 10 were identified with a total of 115 fragments. The populations included in this study showed high levels of genetic variation (H=0.21). According to genetic distance, the most genetically distant populations were Soush and Dezful, and the most similar populations were Sarollah and Musiyan. A. florea populations from three states grouped as only one big cluster on the tree based on Nei genetic distance. Isolation by distance test showed no significant relation between geographic and genetic distance. However, populations within each state showed higher similarities than the populations of other states.