Gestational Diabetes Mellitus (GDM) is defined as any degree of glucose intolerance with onset or first recognition during pregnancy. In view of maternal morbidity and mortality as well as fetal complications, early diagnosis is an utmost necessity in the present scenario. In developing country like India, early detection and prevention will be more cost effective. Oral Glucose Tolerance Test (OGTT) is the crucial method for diagnosing GDM done usually between 24th and 28th week of pregnancy. The proposed work focuses on early detection of GDM without a visit to the hospital for women who are pregnant for the second time onwards (multigravida patients). A decision support system using Multilayer Neural Network which learns to classify GDM and non GDM patients using Back 3328 Priya Shirley Muller et al. Propagation learning algorithm is developed. The classifier proves to be an efficient model for diagnosis of GDM without the conventional method of blood test by providing newly designed parameters as inputs to the network.

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Aim: A trial was conducted to assess the influence of parasitic load on the lambs reared under the intensive system, continuous grazing, and rotational grazing systems of management. Materials and Methods: A total of thirty numbers of the undetermined breed of ewe lambs around 4-5 months of age were randomly selected and allotted to three treatment groups: T1 (intensive system - control), T2 (rotational grazing), and T3 (continuous grazing). The T1 group lambs were raised under a stall-fed system of management, the T2 group lambs were grazed under rotational grazing strategy in four paddocks of plot-A, while the T3 group lambs were continuously grazed in plot-B. Results: At the end of the study, there was a highly significant difference (p=0.01) in the fortnightly strongyle egg count per gram (EPG) of feces among the lambs pertaining to the three treatment groups; the lambs in T3 had a higher strongyle EPG compared to T2 lambs. With regard to the overall reduction in EPG from the initial count, lambs under rotational grazing showed the maximum decrease of 54.52% compared to lambs under T3 (continuous grazing). There was a strong positive correlation noticed between the mean temperature of the day at each fortnight and the subsequent EPG at each fortnight with R2=0.87. There was a strong positive correlation noticed between mean FAMACHA® scores and the EPG with R2=0.84, R2=0.83, and R2=0.83 for T1, T2, and T3, respectively. Conclusion: The grazing management with pasture rotation should be considered as a viable option for sustainable parasitic control in case of grazing-dependent livestock husbandry in India.

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Hadoop is an open-source utility which allows users to provide massive input in terms of data and facilitates the computation. Role of Hadoop in load balancing is enormous which allows the user to configure the network of nodes having master/slave nodes. Hadoop's typical architecture takes into consideration the default configuration for the machine as homogeneous, but many of the real-time application or clusters of nodes will have the homogeneous configurations. Thus, an effort is made in this paper to consider the homogeneity of the nodes in clusters and build an efficient algorithm which does load balancing in an efficient way when compared with the default balancer of Hadoop which works well only on homogeneous nodes.