Why do some fish fight more than others?

Reversible changes in how readily animals fight can be explained in terms of adaptive responses to differences in the costs and benefits of fighting. In contrast, long-term differences in aggressiveness raise a number of questions, including why animals are consistent with respect to this trait, why aggressiveness is often linked to general risk taking, and why aggressive and nonaggressive animals often coexist within a population. In fish, different levels of aggressiveness bring several direct fitness-related consequences, such as when aggressive individuals monopolize a limited food supply and grow fast. They also bring indirect consequences, such as when aggressive fish are more susceptible to predation and when they require a larger respiratory surface to service a higher metabolic rate. Fitness consequences of aggressiveness are often context dependent, with aggressive fish tending to do well in simple, predictable conditions but not in complex, less predictable conditions. The diverse, context-dependent consequences of aggression mean that aggressive and nonaggressive fish flourish in different conditions and explain in general terms why these behavioral phenotypes often coexist. There are a number of candidate evolutionary frameworks for explaining why individual differences in aggressiveness are often, but not always, consistent over time and often, but not always, linked to differences in general risk taking

Efficient Buffer Management Protocol for Multicast Streaming in MANET

AbstractBuffer management techniques are essential while handling multicast streaming in MANET since real-time data will involve playback delay and jitter. In this paper, an efficient buffer management protocol is developed for streaming data in multicast groups. The frequently requested video data can be buffered in any intermediate nodes along the multicast tree from the source to the receivers. When packets are received, they are classified as real-time or non-real-time and placed into respective queues. Cumulative weight of the packets in the real-time buffer is then estimated based on number of hops, deadline and waiting time. Based on the estimated weight value, transmission priorities are assigned. The buffer space is dynamically adjusted depending on the number of intermediate nodes along the multicast tree from the source to the receivers. Simulation results show that the proposed buffer management protocol reduces the latency and energy consumption while increasing the packet delivery ratio

Higher order numerical method for a semilinear system of singularly perturbed differential equations

In this paper, a system of singularly perturbed second order semilinear differential equations with prescribed boundary conditions is considered. To solve this problem, a parameter-uniform numerical method is constructed which consists of a classical finite difference scheme and a piecewise uniform Shishkin mesh. It is proved that the convergence of the proposed numerical method is essentially second order in the maximum norm. Numerical illustration presented supports the proved theoretical results

Smart Asset Management for Electric Utilities: Big Data and Future

This paper discusses about future challenges in terms of big data and new technologies. Utilities have been collecting data in large amounts but they are hardly utilized because they are huge in amount and also there is uncertainty associated with it. Condition monitoring of assets collects large amounts of data during daily operations. The question arises "How to extract information from large chunk of data?" The concept of "rich data and poor information" is being challenged by big data analytics with advent of machine learning techniques. Along with technological advancements like Internet of Things (IoT), big data analytics will play an important role for electric utilities. In this paper, challenges are answered by pathways and guidelines to make the current asset management practices smarter for the future.Comment: 13 pages, 3 figures, Proceedings of 12th World Congress on Engineering Asset Management (WCEAM) 201

Forecasting Stock Market Volitility- Evidence From Muscat Security Market Using Garch Models

Engle (1982) introduced the autoregressive conditionally heteroskedastic model for quantifying the conditional volatility and by Boollerslev (1986), Engle, Lilien and Robins (1987) and Glosten, Jaganathan and Runkle (1993) extended the class asymmetric model. Amongst many others, Bollerslev, Chou and Kroner (1992) or (1994) are considered to be the précis of ARCH family models. In this direction the paper forecasts the stock market volatility of four actively trading indices from Muscat security market by using daily observations of indices over the period of January 2001 to November 2015 using GARCH(1,1), EGARCH(1,1) and TGARCH (1,1) models. The study reveals the positive relationship between risk and return. The analysis exhibits that the volatility shocks are quite persistent. Further the asymmetric GARCH models find a significance evidence of asymmetry in stock returns. The study discloses that the volatility is highly persistent and there is asymmetrical relationship between return shocks and volatility adjustments and the leverage effect is found across all flour indices. Hence the investors are advised to formulate investment strategies by analyzing recent and historical news and forecast the future market movement while selecting portfolio for efficient management of financial risks to reap benefit in the stock market