Environmentally friendly copper oxide nanoparticles (CuO NPs) were prepared with a green synthesis route via Anchusa strigosa L.  Flowers extract. These nanoparticles were further characterized by FTIR, XRD and SEM techniques. Removing of Gongo red from water was applied successfully by using synthesized CuO NPs which used as an adsorbent material. It was validated that the CuO NPs eliminate Congo red by means of adsorption, and the best efficiency of adsorption was gained at pH (3). The maximum adsorption capacity of CuO NPs for Congo red was observed at (35) mg/g. The equilibrium information for adsorption have been outfitted to the Langmuir, Freundlich, Temkin and Halsey adsorption isot

Charge transfer complex formation method has been applied for the spectrophotometric determination of erythromycin ethylsuccinate, in bulk sample and dosage form. The method was accurate, simple, rapid, inexpensive and sensitive depending on the formed charge- transfer complex between cited drug and, 2,3- Dichloro-5,6-dicyano-p- benzoquinone (DDQ) as a chromogenic reagent. The formed complex shows absorbance maxima at 587 nm against reagent blank. The calibration graph is linear in the ranges of (10 - 110) μg.mL-1 with detection limit of 0.351μg.mL-1. The results show the absence of interferences from the excipients on the determination of the drug. Therefore the proposed method has been successfully applied for the determination of eryth

الصيغة العامة للمعقدات الجديدة [M2(BDS)Cl4] الناتجة من تفاعل الليكاند الجديد] ن1,ن4-ثنائي(1أ –بنزو]د[ اميدازول-2-يل)-ن1,ن4-ثنائي(4-ثنائي مثيل امينو) بنزيل) سكسنمايد[ (BDS) مع الايونات الفلزية الكادميوم, الكوبلت, الزئبق, النحاس والنيكل. تم اشتقاق هذا الليكاند من تفاعل المواد الثلاث 4-(ثنائي ميثيل أمينو) بنزالدهيد، 2-أمينو بنزيميدازول، وكلوريد السكسينيل. تم تشخيص المركبات باستخدام مطيافية طيف الاشعة تحت الحمراء وطيف الرن

      Detecting protein complexes in protein-protein interaction (PPI) networks is a challenging problem in computational biology. To uncover a PPI network into a complex structure, different meta-heuristic algorithms have been proposed in the literature. Unfortunately, many of such methods, including evolutionary algorithms (EAs), are based solely on the topological information of the network rather than on biological information. Despite the effectiveness of EAs over heuristic methods, more inherent biological properties of proteins are rarely investigated and exploited in these approaches. In this paper, we proposed an EA with a new mutation operator for complex detection problems. The proposed mutation operator is formulate