The aim of this study was to isolate and identify the cyanobacterium Scytonema hofmanni Var. calcicolum from the domestic drinking tanks as a new record in Iraqi drinking water. Scytonema hofmanni var. calcicolum, a filamentous freshwater cyanobacterium (blue-green alga). This alga was isolated from the walls of the domestic plastic water tanks in Al- karkh/ Baghdad city on July 2014. The sampling was performed by collecting three samples from this tanks, the three examined samples microscopically revealed the dominance of this cyanobacterium as unialgal in the studied samples. The results showed this alga has the ability to tolerate high temperature up to 42 Cº and very low light intensity inside the tanks which up to 10 μE/m²/s.

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

In Indonesia, cattle feces (CF) and water hyacinth (WH) plants are abundant but have not been widely revealed. The use of microorganisms as decomposers in the fermentation process has not been widely applied, so researchers are interested in studying further. This study was to evaluate the effect of the combination of CF with WH on composting by applying white-rot fungal (WRF) (Ganoderma sp) microorganism as a decomposer. A number of six types of treatment compared to R₁(ratio of CF:WH)(25%:75%)+WRF; R₂(ratio of CF:WH)(50%:50%)+WRF; R₃(ratio of CF:WH)(75%:25%)+WRF; R₄(ratio of CF:WH)(25%:75%) without WRF; R₅(ratio of CF:WH)(50%:50%) without WRF; R₆(ratio of CF:WH)

The objective of the present study is to verify the actual carious lesion depth by laser
fluorescence technique using 650 nm CW diode laser in comparison with the histopathological
investigation. Five permanent molar teeth were extracted from adult individuals for different reasons
(tooth impaction, periodontal diseases, and pulp infections); their ages were ranging from 20-25 years
old. Different carious teeth with varying clinical stages of caries progression were examined. An
experimental laser fluorescence set-up was built to perform the work regarding in vitro detection and
quantification of occlusal dental caries and the determination of its actual clinical carious lesion depth by
650 nm CW diode laser (excitat

Pseudomonas aeruginosa is the most common opportunistic pathogen causing morbidity and mortality in hospitalized patients due to its multiple resistance mechanisms. Therefore, as a therapeutic option becomes restricted, the search for a new agent is a preference. So P. aeruginosa is an extremely versatile Gram-negative bacterium capable of thriving in a broad spectrum of environments, and this performs main problems to workers in the field of health. One hundred and fifty samples were collected from different sources from Baghdad hospitals, divided into two main groups: clinical (100) specimens and (50) samples as an environmental, collected from October 2019 to the March 2020. All of these samples were cultured by specific and differential

Reverse Phase High Performance Liquid Chromatography (RP-HPLC) was coupled with ultraviolet absorption sepectoscopy (UV) for separation and identification of Naphthalene, Acenaphthylene, Pyrene, Benz{a} anthracene and 1,3,2,4-Dibenzanthracene. RP-HPLC was performed on an ODS-C18 column (150×4.6 mm I.D) using acetonitrile–buffer phosphate as mobile phase. UV absorption spectra of the elutes was detected in 254 nm, and studying the chromatographic variables include organic modifier ratio, PH, column temperature and concentration of buffer to maximize resolution and minimize separation time. the results showed that using mobile phase( 80:20) v/v acetonitrile:0.01M phosphate buffer solution at PH 6 with flow rate 1ml/min and column te

This paper proposes improving the structure of the neural controller based on the identification model for nonlinear systems. The goal of this work is to employ the structure of the Modified Elman Neural Network (MENN) model into the NARMA-L2 structure instead of Multi-Layer Perceptron (MLP) model in order to construct a new hybrid neural structure that can be used as an identifier model and a nonlinear controller for the SISO linear or nonlinear systems. Two learning algorithms are used to adjust the parameters weight of the hybrid neural structure with its serial-parallel configuration; the first one is supervised learning algorithm based Back Propagation Algorithm (BPA) and the second one is an intelligent algorithm n