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Introduction. The research was carried out to check the impact on the commercial refining of sunflower oil such as crude, neutralization, bleaching and deodorization on some specific physicochemical attributes that are essential for quality point of view and health. Materials and methods. Sunflower (SFO) oil samples (crude, neutralized, bleached and deodorized) were collected from the processing line unit from industrial oil company. In this study physicochemical parameters of SFO have been determined by official IUPAC and AOCS methods, while fatty acid composition was checked on GC-MS. Results and discussion. The obtained outcome of physical parameters showed that neutralization, bleaching and deodorization steps of crude SFO considerably decreased the moisture content (0.46-0.04%), color (2.8-1.1R, 28.0-11.0Y), freezing point (3.2-2.3) and smoke point (226.0-219.0), while some lesser change in refractive index was also observed. Moreover, in the case of chemical parameters for instance free fatty acids, saponification value and peroxide value were reduced from 0.56 to 0.06%, 178.5 to 177.2 mg KOH/g oil and 3.2 to 0.9 mEqO 2 /Kg oil, correspondingly. On the other hand refining steps did not showed significant impact on the iodine value (126.0-125.2 gI 2 /100g of oil) and fatty acid composition (total unsaturated fatty acids 89.06-90.91%). The most important influence during industrial processing was noted in soap contents, as these are generated during second step of refining. In present study soap content were reduced from 121.0 to 30.4 ppm during neutralization to deodorization steps. Conclusions. Among industrial process, deodorization step has greater influence on physicochemical attributes on the quality and stability of processed SFO.

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For the last few decades, Wireless Sensor Networks (WSNs) has been drawing important considerations due to having application-specific characteristics. These WSNs are usually deployed in one of the following two manners: deterministic or random (ad hoc). In the ad hoc manner, the deployment is mostly subjected to a significant number of limitations such as limited bandwidth, routing failure, storage and computational constraints. The overall performance of the WSNs is determined by a robust routing scheme. Nevertheless, WSNs include prominent application parameters for routing such as energy usage and network longevity. Therefore, the routing scheme is the key element for the longevity and usability of WSNs. In the conventional WSNs, the routing design can be opted for the network longevity optimization, while, assuming all the other objectives to be the limitations are imposed on the optimization problem Genetic Algorithm (GA) performs the small-scale computation and large-scale computation as well. Performance of GA is robust in both small scale and large scale computations. The original GA is assumed with some modifications. In this paper, a GA based optimization in the stationary WSNs with the deployment of multiple sinks is proposed. It is assumed that the sensor nodes route the data towards the nearest sink through the multiple hops communication strategy. In our simulations results: routing is following the multiple hops to the sink by the optimized routing. Moreover, we've enhanced the Network lifespan. The proposed technique saved both the route distance through optimization and energy by routing the data through optimized neighbor sensor nodes.

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