This study was carried out to determine the heavy metal accumulation of Juncus rigidus Desfontaines, 1798 from three different regions of the Basrah Province in Southern of Iraq. Specifically, the concentrations of lead, nickel, and cadmium were determined in the roots, culms and leaves of the plant. The results indicated that the highest accumulation of the heavy metal was recorded in lead (Pb) 12.50± 3.58 mg kg^-1and then in nickel (< 0.30). The lowest value was recorded for cadmium (< 0.05). As well, lead concentrations in J. rigidus varied in different locations and parts of the plant from undetectable in control to 12.66, 19.33, and 9.80 mg kg^-1 in leaves, culm, and roots respective

In Incremental sheet metal forming process, one important step is to produce tool path, an
accurate tool path is one of the main challenge of incremental sheet metal forming
process. Various factors should be considered prior to generation of the tool path i.e.
mechanical properties of sheet metal, the holding mechanism, tool speed, feed rate and
tool size. In this work investigation studies have been carried out to find the different tool
path strategies to control the twist effect in the final product manufactured by single point
incremental sheet metal forming (SPIF), an adaptive tool path strategy was proposed and
examined for several Aluminum conical models. The comparison of the proposed tool path with t

In this research the results of applying Artificial Neural Networks with modified activation function to
perform the online and offline identification of four Degrees of Freedom (4-DOF) Selective Compliance
Assembly Robot Arm (SCARA) manipulator robot will be described. The proposed model of
identification strategy consists of a feed-forward neural network with a modified activation function that
operates in parallel with the SCARA robot model. Feed-Forward Neural Networks (FFNN) which have
been trained online and offline have been used, without requiring any previous knowledge about the
system to be identified. The activation function that is used in the hidden layer in FFNN is a modified
version of the wavelet func

In this research the results of applying Artificial Neural Networks with modified activation function to perform the online and offline identification of four Degrees of Freedom (4-DOF) Selective Compliance Assembly Robot Arm (SCARA) manipulator robot will be described. The proposed model of identification strategy consists of a feed-forward neural network with a modified activation function that operates in parallel with the SCARA robot model. Feed-Forward Neural Networks (FFNN) which have been trained online and offline have been used, without requiring any previous knowledge about the system to be identified. The activation function that is used in the hidden layer in FFNN is a modified version of the wavelet function. This approach ha