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Aim: The present investigation was conducted for evaluating the efficacy of estimating breeding values (BVs) using univariate animal model in comparison to sire model. The various parameters considered for evaluating the efficiency of both linear models were coefficient of determination (R 2 ), coefficient of variation (CV), within sire variance or error variance and spearman's rank correlation estimates. Materials and Methods:Data for the present investigation were spread over a period of 34 years from 1978 to 2012 and consisted of a total of 1988 lactation records of Holstein Friesian crossbred cows sired by 186 crossbred bulls. Results:The percentage of sires having BV (estimated by animal model) more than the average BV for the traits viz. age at first calving (AFC), first service period (FSP), first lactation length (FLL), first calving interval (FCI), first lactation 305 day milk yield (F305MY), first lactation total milk yield (FTMY), milk yield per day of first lactation length (MY/FLL) and milk yield per day of first calving interval (MY/FCI) were 48. 05, 49.39, 55.07, 49.21, 50.00, 51.39, 48.67, 50.39%, respectively. The animal model had higher R 2 , lower CV and error variance for most of the fertility and production traits. The spearman's rank correlation estimates indicated similarity of rankings by both the linear models as the animal model is an improvement of sire model. Conclusion:Animal model had a wider range of BVs indicating the greater differentiating ability of the model. Based on R 2 , CV and error variance animal model was found to be superior in comparison to sire model.

The unprecedented increase of wireless devices is now facing a serious threat of spectrum scarcity. The situation becomes even worse due to inefficient frequency distribution protocols, deployed in trivial Wi-Fi networks. The primary source of this inefficiency is static channelization used in wireless networks. In this work, we investigate the use of dynamic and flexible channelization, for optimal spectrum utilization in Wi-Fi networks. We propose optimal spectrum sharing algorithm (OSSA) and analyze its effect on exhaustive list of essential network performance measuring parameters. The elementary concept of the proposed algorithm lies in the fact that frequency spectrum should be assigned to any access point (AP) based on its current requirement. The OSSA algorithm assigns channels with high granularity, thus maximizing spectrum utilization by more than 20% as compared to static width channel allocation. This optimum spectrum utilization, in turn, increases throughput by almost 30% in many deployment scenarios. The achieved results depict considerable decrease in interference, while simultaneously increasing range. Similarly signal strength values at relatively longer distances improve significantly at narrower channel widths while simultaneously decreasing bit error rates. We found that almost 25% reduction in interference is possible in certain scenarios through proposed algorithm.

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