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Feeding potential and foraging behavior are complimentary to determine the efficiency of a predator. The predatory potential of both grub and adult stages of Cheilomenes sexmaculata (F.) (Coleoptera: Coccinellidae) was investigated against different developmental stages of cotton whitefly, Bemisia tabaci Gennadius (Aleyrodidae: Hemiptera), under laboratory conditions (27 ± 2 °C; 70 ± 5% RH; 14 h L: 10 h D). Fourth instar grub of C. sexmaculata exhibited the highest feeding potential against the whitefly stages (41.74 adults, 33.41 pupae and 31.33 nymphs), whereas first instar grub exhibited the lowest feeding potential (10.15 adults, 9.15 pupae and 7.00 whitefly nymphs). The female beetle consumed the highest number of prey (1445.29 adults, 1364.88 pupae, and 1374.35 whitefly nymphs) followed by male beetle (1390.12 adults, 1308.18 pupae and 1325.35 whitefly nymphs). Both Roger's and Holling's models indicated a type II functional response curve for all the grub and adult stages of C. sexmaculata. Cheilomenes sexmaculata female showed the highest attack rate (4.65 h-1), and the maximum rate of predation 37.5 insects/day, whereas the male beetle recorded the lowest handling time (0.02 h). The fourth instar grub showed the highest attack rate (1.30 h-1) against the B. tabaci pupae, whereas the female beetle showed the maximum rate of predation 38.18 insects/day, with the lowest handling time (0.04 h). Female beetle showed the highest attack rate (6.87 h-1), whereas the fourth instar grub showed the maximum rate of predation 41.7 insects/day with the lowest handling time (0.01 h). Hence, it is advocated to release the fourth instar grubs and adult females of C. sexmaculata collectively for the augmentative biological control of B. tabaci in the IPM program. [ABSTRACT FROM AUTHOR]

Introduction: Medical image processing aimed at reducing human error rates attracted many researchers. The Segmentation of magnetic resonance image for tumor detection is one of the recognized challenges in the treatment of the disease. Considering the importance of this issue in the present study, the diagnosis of brain tumor is considered. Material and Methods: One of the most popular and most widely used methods in the field of segmentation of images of resonance imaging of the brain is the kmeans clustering algorithm, which, despite the diagnosis of a tumor, fall in to local optimum problem, followed by a reduction in the accuracy of the diagnosis tumors are malignant. In this study, we aimed to solve this problem and subsequently increase the accuracy of diagnosis of malignant tumors, a GAclustering combination of clustering based on k-means and genetic algorithms. Results: How to combine in the way that the genetic algorithm is applied to each repetition of the K-means algorithm and, by scanning more in the space of the answer, is trying to find higher quality cluster centers. The effectiveness of the proposed method has been investigated on a number of images of BRATS standard collections. It is also compared with the K-means algorithm. Conclusion: The results show that the proposed algorithm provides better results than the K-means algorithm. [ABSTRACT FROM AUTHOR]