Thirty-two yeast isolates were retrieved from four soil samples collected from hydrocarbon-polluted locations of Hisar, Haryana, using enrichment culture technique with 1% (v/v) diesel as carbon source. Total nine isolates showing blood agar haemolysis were screened further for biosurfactant production. Yeast isolate, YK32, gave highest 8.4-cm oil displacement which was found to be significantly higher as compared to positive control, 0.2% (w/v) SDS (6.6 cm), followed by 6.2 and 6.0 cm by isolates YK20 and YK21, respectively. Maximum emulsification index was obtained in case of isolates YK20 and YK21 measuring 53.8%, after 6 days of incubation utilizing glucose as carbon source, whereas isolate YK32 was found to be reducing surface tension up to 93 dynes/cm and presented 99.6% degree of hydrophobicity. Olive oil has supported maximum surface tension reduction in isolates YK32 and YK21 equivalent to 53 and 48 dynes/cm and gave 88.3 and 88.5% degree of hydrophobicity, respectively. Diesel was not preferred as carbon source by most of the isolates except YK28 which generated 5.5-cm oil displacement, 25% emulsification index, reduced surface tension to the level of 38 dynes/cm and presented 89% degree of hydrophobicity. Conclusively, isolates YK20, YK21, YK22 and YK32 were marked as promising biosurfactant producers and were subjected to identification. Based on microscopic examination and biochemical peculiarities, isolates YK21 and YK22 might be identified as Candida spp., whereas, isolates YK20 and YK32 might be identified as Saccharomycopsis spp. and Brettanomyces spp., respectively. Interestingly it is the first report indicating Saccharomycopsis spp. and Brettanomyces spp. as a potential biosurfactant producer.

This paper aims to establish a solution methodology for the optimal design of digital infinite impulse response (IIR) filters by integrating the features of cat swarm optimization (CSO) and differential evolution algorithm (DE). DE is a population based stochastic optimization technique which optimizes real valued functions. It requires negligible control parameter tuning but sometimes causes instability problem. CSO is a heuristic optimization algorithm based on the observations and imitation of the natural behavior of cats. CSO algorithm possesses local as well as global search capabilities. Although, CSO possesses better capability to search optimal point but it requires a higher computation time because the local and global searches are carried out independently in each iteration. A hybrid algorithm is proposed using the CSO algorithm and the DE optimization algorithm for the robust and stable design of digital IIR filter. To start with a better solution set, opposition based learning strategy is incorporated. The proposed method explores and exploits the search space locally as well as globally. The design criterion undertakes the minimization of magnitude approximation error and ripple magnitudes of both pass-band and stop-band satisfying the stability requirements. The developed hybrid algorithm is effectively applied for designing the digital low-pass, high-pass, band-pass and band-stop filters. The computational results demonstrate that the proposed algorithm is capable of creating designs that are competitive with reference to other design processes and can efficiently be applied for higher order filter design.

Denial of service attacks produce very large amount of packets by a large amount of agents, these packets can easily interfere with the original flow and communication source within very short duration of time. The low rate distributed denial of service attacks (LDDoS) is those which produce less amounts of packet to attack flows. They are a combination of a large number of LDoS flows. There are several techniques called queuing algorithms which are introduced to fight LDDoS attacks. In our research work we will compare some of these queuing management techniques to find the best among them. This review paper will be helpful in our further work.

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