The importance of algae as a natural source of various bioactive compounds continues to grow due to their diverse biological activities. One of such compounds is n-hexadecanoic acid, more commonly known as palmitic acid; it is more lipidous than many algal species. It is one of the most common saturated fatty acids in nature. One can find it in algal, animal, and plant oils and is extracted from numerous natural sources. Its chemical and biological attributes enable its widespread use in the pharmaceutical, food, and cosmetic industries. Numerous studies have found that palmitic acid is effective for fungal activities and does have pharmaceutical and environmental promising antioxidant capabilities.

   Asphaltenes are a solubility class described as a component of crude oil with undesired characteristics. In this study, Sharqy Baghdad heavy oil upgrading was achieved utilizing the solvent deasphalting approach as asphaltenes are insoluble in paraffinic solvents; they may be removed from heavy crude oil by adding N-Hexane as a solvent to create deasphalted oil (DAO)of higher quality. This method is known as Solvent De-asphalting (SDA). Different effects have been assessed for the SDA process, such as solvent to oil ratio (4-16/1 ml/g), the extraction temperature (23 ºC) room temperature and (68 ºC) reflux temperature at (0.5 h mixing time with 400 rpm mixing speed). The best solvent deasphalting results were obtained at room

Amaranthus viridis (F. Amaranthaceae), usually known as slender amaranth. It is a multifunctional vegetable that grows in tropical and subtropical areas, sustaining stressful conditions, especially drought, producing a wide array of secondary metabolites: phenolics, terpenes, carotenoids and others, which are responsible for various therapeutic effects.  Aerial parts were defatted with hexane, then the defatted residue was extracted in the Soxhlet using 80 % aqueous ethanol. Ethanolic extract was sequentially and repeatedly partitioned with solvents of different polarities: chloroform, ethyl acetate and n-butanol. Then, the n-butanol fraction was hydrolyzed using reflex with 10 % aqueous HCl for 6 hrs. The unhydrolyzed and hydrolyz

Recently, Image enhancement techniques can be represented as one of the most significant topics in the field of digital image processing. The basic problem in the enhancement method is how to remove noise or improve digital image details. In the current research a method for digital image de-noising and its detail sharpening/highlighted was proposed. The proposed approach uses fuzzy logic technique to process each pixel inside entire image, and then take the decision if it is noisy or need more processing for highlighting. This issue is performed by examining the degree of association with neighboring elements based on fuzzy algorithm. The proposed de-noising approach was evaluated by some standard images after corrupting them with impulse

In this paper two ranking functions are employed to treat the fuzzy multiple objective (FMO) programming model, then using two kinds of membership function, the first one is trapezoidal fuzzy (TF) ordinary membership function, the second one is trapezoidal fuzzy weighted membership function. When the objective function is fuzzy, then should transform and shrinkage the fuzzy model to traditional model, finally solving these models to know which one is better

Simulation  of  the  Linguistic  Fuzzy Trust  Model  (LFTM)  over  oscillating  Wireless  Sensor Networks (WSNs) where the goodness of the servers belonging to them could change along the time is presented in this paper, and the comparison between the outcomes achieved with LFTM model over oscillating WSNs with the outcomes obtained by applying the model over static WSNs where the servers maintaining always the same goodness, in terms of the selection percentage of trustworthy servers (the accuracy of the model) and the average path length are also presented here. Also in this paper the comparison between the LFTM and the Bio-inspired Trust and Reputation Model for Wireless Sensor Network

Producing pseudo-random numbers (PRN) with high performance is one of the important issues that attract many researchers today. This paper suggests pseudo-random number generator models that integrate Hopfield Neural Network (HNN) with fuzzy logic system to improve the randomness of the Hopfield Pseudo-random generator. The fuzzy logic system has been introduced to control the update of HNN parameters. The proposed model is compared with three state-ofthe-art baselines the results analysis using National Institute of Standards and Technology (NIST) statistical test and ENT test shows that the projected model is statistically significant in comparison to the baselines and this demonstrates the competency of neuro-fuzzy based model to produce