This paper studies the effects of stiffeners on shear lag in steel box girders with stiffened flanges. A three-dimensional linear finite element analysis using STAAD.Pro V8i program has been employed to evaluate and determine the actual top flange stress distribution and effective width in steel box girders. The steel plates of the flanges and webs have been modeled by four-node isoparametric shell elements, while the stiffeners have been modeled as beam elements. Different numbers (4, 8, and 15) for the steel stiffeners have been used in this study to establish their effects on the shear lag and longitudinal stresses in the flange. Using stiffeners reduced the magnitude of the top flange longitudinal stresses about 40%, but did

Recently, important efforts have been made in an attempt to search for the cheapest and ecofriendly alternatives adsorbents. In the present work, waste molasses from Iraqi date palm (Zahdi) had been used as a provenance to produce charcoal for the removal of methylene blue (MB) dye from water. The optimum prepared charcoal was obtained at 150 C, by increasing temperature to 175 C, the charcoal had almost converted to ash. The obtained charcoal have been inspected for properties using scanning electron microscope (SEM), atomic force microscope (AFM), porosity and surface area. Adsorption data were optimized to Langmuir and Freundlich and adsorption parameters have been evaluated. The thermodynamic parameters like a change

Let M be a weak Nobusawa -ring and γ be a non-zero element of Γ. In this paper, we introduce concept of k-reverse derivation, Jordan k-reverse derivation, generalized k-reverse derivation, and Jordan generalized k-reverse derivation of Γ-ring, and γ-homomorphism, anti-γ-homomorphism of M. Also, we give some commutattivity conditions on γ-prime Γ-ring and γ-semiprime Γ-ring .

       Laboratory experiments were carried  out in Technical college AL- Mussiab / Babylon during 2005  to study bioactivity of different concentration from ( aqueous , alcohol and hexane) extracts of bee venom collected from different workers of bees against some bacterial types that cause European Foul – brood Melissococcus plutom , Bacillus alvei and B. letrosporus which had been isolated from infected arched with disease . Two diffusion methods (digging and paper discs) were followed for the extraction of the venom . Results showed that digging diffusion method was more efficient for  test of bacterial inhibition which led to increase the activity of bee venom extract with general a

Conducted two trials separate plants Defla first two seasons, 1998 and 1999 to test the susceptibility Altgveria three varieties including Azharha colored white and pink Qati and pink Qtmr and second seasons 1999 and 2000, two types color Azhaarhama white and pink Qati treated mind half-timbered two types of Alaoxinat IBA and NAA and three concentrations as well as repeatersAdhrt results low Almaah rooting

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.

By driven the moment estimator of ARMA (1, 1) and by using the simulation some important notice are founded, From the more notice conclusions that the relation between the sign   and moment estimator for ARMA (1, 1) model that is: when the sign is positive means the root      gives invertible model and when the sign is negative means the root      gives invertible model. An alternative method has been suggested for ARMA (0, 1) model can be suitable when

Biometrics represent the most practical method for swiftly and reliably verifying and identifying individuals based on their unique biological traits. This study addresses the increasing demand for dependable biometric identification systems by introducing an efficient approach to automatically recognize ear patterns using Convolutional Neural Networks (CNNs). Despite the widespread adoption of facial recognition technologies, the distinct features and consistency inherent in ear patterns provide a compelling alternative for biometric applications. Employing CNNs in our research automates the identification process, enhancing accuracy and adaptability across various ear shapes and orientations. The ear, being visible and easily captured in

Face recognition is required in various applications, and major progress has been witnessed in this area. Many face recognition algorithms have been proposed thus far; however, achieving high recognition accuracy and low execution time remains a challenge. In this work, a new scheme for face recognition is presented using hybrid orthogonal polynomials to extract features. The embedded image kernel technique is used to decrease the complexity of feature extraction, then a support vector machine is adopted to classify these features. Moreover, a fast-overlapping block processing algorithm for feature extraction is used to reduce the computation time. Extensive evaluation of the proposed method was carried out on two different face ima