The title compound, [Ru(C12H7Br2N2)2(CO)2], possesses a distorted octahedral environment about the Ru atom, with two cyclometallated 4,40-dibromoazobenzene ligands and two mutually cis carbonyl ligands. The donor atoms are arranged such that the N atoms are respectively trans to a carbonyl ligand and an aryl C atom. Comment The title compound, (I), has been prepared as a minor product of the reaction of Ru3(CO)12 and 4,40-dibromoazobenzene in refluxing n-octane; the major product is the cluster complex Ru3(3-NC6H4Br)2(CO)9 (Willis et al., 2005). Two strong (CO) absorptions at 2039 and 1991 cm1 in the IR spectrum of (I) are consistent with the presence of two mutually cis carbonyl groups. The crystal structure was investigated to ascertai

ABSTRACT. 4-Sulfosalicylic acid (SSA) was used as a ligand to prepare new triphenyltin and dimethyl-tin complexes by condensation with the corresponding organotin chloride salts. The complexes were identified by different techniques, such as infrared spectra (tin and proton), magnetic resonance, and elemental analyses. The 119Sn-NMR was studied to determine the prepared complexes' geometrical shape. Two methods examined the antioxidant activity of (SSA) and prepared complexes; Free radical scavenging activity (DPPH) and CUPRRAC methods. Tri and di-tin complexes gave high percentage inhibition than ligands with both methods due to tin moiety; the triphenyltin carboxylate complex was the best compared with the others. Also, antibacter

Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D

The preparation of the title compound, C₂₆H₂₅N, was achieved by the condensation of an ethanolic mixture of benzaldehyde, cyclohexanone and ammonium acetate in a 2:1:1 molar ratio. There are two crystallographically independent molecules in the asymmetric unit. The two cyclohexyl rings adopt ananti-envelope conformation with the benzyl moiety adopting acisconformation with respect to the nitrogen atom of the phenanthridine segment. In the crystal, molecules are linked through C—H...N interactions into hydrogen-bonded chains that are further arranged into distinct layers by weak offset π–π interactions.

An application of neural network technique was introduced in modeling the point efficiency of sieve tray, based on a
data bank of around 33l data points collected from the open literature.Two models proposed,using back-propagation
algorithm, the first model network consists: volumetric liquid flow rate (QL), F foctor for gas (FS), liquid density (pL),
gas density (pg), liquid viscosity (pL), gas viscosity (pg), hole diameter (dH), weir height (hw), pressure (P) and surface
tension between liquid phase and gas phase (o). In the second network, there are six parameters as dimensionless
group: Flowfactor (F), Reynolds number for liquid (ReL), Reynolds number for gas through hole (Reg), ratio of weir
height to hole diqmeter

The aim of this paper is to design suitable neural network (ANN) as an alternative accurate tool to evaluate concentration of Copper in contaminated soils. First, sixteen (4x4) soil samples were harvested from a phytoremediated contaminated site located in Baghdad city in Iraq. Second, a series of measurements were performed on the soil samples. Third, design an ANN and its performance was evaluated using a test data set and then applied to estimate the concentration of Copper. The performance of the ANN technique was compared with the traditional laboratory inspecting using the training and test data sets. The results of this study show that the ANN technique trained on experimental measurements can be successfully applied to the rapid est

Whenever, the Internet of Things (IoT) applications and devices increased, the capability of the its access frequently stressed. That can lead a significant bottleneck problem for network performance in different layers of an end point to end point (P2P) communication route. So, an appropriate characteristic (i.e., classification) of the time changing traffic prediction has been used to solve this issue. Nevertheless, stills remain at great an open defy. Due to of the most of the presenting solutions depend on machine learning (ML) methods, that though give high calculation cost, where they are not taking into account the fine-accurately flow classification of the IoT devices is needed. Therefore, this paper presents a new model bas

The matter of handwritten text recognition is as yet a major challenge to mainstream researchers. A few ways deal with this challenge have been endeavored in the most recent years, for the most part concentrating on the English pre-printed or handwritten characters space. Consequently, the need to effort a research concerning to Arabic texts handwritten recognition. The Arabic handwriting presents unique technical difficulties because it is cursive, right to left in writing and the letters convert its shapes and structures when it is putted at initial, middle, isolation or at the end of words. In this study, the Arabic text recognition is developed and designed to recognize image of Arabic text/characters. The proposed model gets a single l