The study includs,effect of concentration of Lead 0.2 ,0.3 , 0.5, 5 , 10 mg/L and Zinc 0.1,0.5 , 2 , 4 , 8 mg/L lonely or to gether on growth green algae( Scenedesmus quadricauda var . longispina) according to the total qauntity for the cells and the adsorption of the algae to the zn,pb concentration .growth curve and dubbling time growth were calculated with or without there heavy metals . Results shows that there are significant differences (P<0.01) for growth curve and the control. (7.5201 cell /h)and with dubbling times (9.87 cell/h). The heavy metals(Pb, Zn). shows antagonistic effect when both used in media.

The present study deals with the optimum design of self supporting steel communication towers. A special technique is used to represent the tower as an equivalent hollow tapered beam with variable cross section. Then this method is employed to find the best layout of the tower among prespecified configurations. The formulation of the problem is applied to four types of tower layout
with K and X brace, with equal and unequal panels. The objective function is the total weight of the tower. The variables are the base and the top dimensions, the number of panels for the tower and member's cross section areas. The formulations of design constraints are based on the requirements of EIA and ANSI codes for allowable stresses in the members

The study area soils suffer from several problems appear as tkhesvat and cracks in the roads and waterlogging which reduces the susceptibility of soil to withstand pressure, this study was conducted on the soil of the Karkh district based on field study that included (6) samples of soil physical analyses contain different ratios of (mud, sand, silt) as percentages (52%, 45%, 3 #) respectively, and liquidity limit rate (39%) Stroke rate plasticity was (20.6%) The rate coefficient of plasticity total (19.2%)0

Lead selenide PbSe thin films of different thicknesses (300, 500, and 700 nm) were deposited under vacuum using thermal evaporation method on glass substrates. X-ray diffraction measurements showed that increasing of thickness lead to well crystallize the prepared samples, such that the crystallite size increases while the dislocation density decreases with thickness increasing. A.C conductivity, dielectric constants, and loss tangent are studied as function to thickness, frequency (10kHz-10MHz) and temperatures (293K-493K). The conductivity measurements confirm confirmed that hopping is the mechanism responsible for the conduction process. Increasing of thickness decreases the thermal activation energy estimated from Arhinus equation is

Etodolac is choice of drug for pain and inflammation but has major side effects of gastric ulcers that are due to free carboxylic group. Etodolac belongs to the chemical class of non-selective COX-inhibitor but preferentially COX-2 inhibitor. Here the ester linked mutual prodrugs of etodolac with phytophenols like vanillin, carvacrol, umbelliferone, guaiacol, sesamol and syringaldehyde were synthesized. All the prodrugs were characterized by IR-spectroscopy, ¹H-NMR, ¹³C-NMR and mass spectrometry. Among the synthesized prodrugs, the Eto-van, Eto-umbe, Eto-sesa and Eto-syr showed improved analgesic and anti-inflammatory activity compared to etodolac. All the synthesized prodrugs showed less ulcerogenic side effects co

Value Engineering is an analytical study on projects or services using a specific procedure and a multidisciplinary working group, works for the identification and classification of the project functions; either for a better perfuming of these functions or to lessen the total project cost or the two together. Value Engineering main aim is on finding innovative alternatives, without effecting the basic requirements of the project, its methodology based on the functional balancing between the three elements of production "performance, quality and cost". This methodology based on the "functional analysis", had shown high possibilities in solving any problem facing the production procedure , achieve better investment for available re

This paper includes an experimental study of hydrogen mass flow rate and inlet hydrogen pressure effect on the fuel cell performance. Depending on the experimental results, a model of fuel cell based on artificial neural networks is proposed. A back propagation learning rule with the log-sigmoid activation function is adopted to construct neural networks model. Experimental data resulting from 36 fuel cell tests are used as a learning data. The hydrogen mass flow rate, applied load and inlet hydrogen pressure are inputs to fuel cell model, while the current and voltage are outputs. Proposed model could successfully predict the fuel cell performance in good agreement with actual data. This work is extended to developed fuel cell feedback