Abstract: Mixed ligand Mn(II), Co(II), Ni(II), Cu (II), Zn(II), and Cd(II) complexes with (TMAP) Schiff base ligand and (8HQ) have been composition and analyzed. Diagnosis by, melting point, solubility, Electronic, mass and IR-spectroscopic studies, conductivity elemental, thermoanalytical analysis displayed the forming of mononuclear complexes. Spectral studies results suggest an octahedral system or the metal (II) mixed complexes. The detainments of molar conductance of the mixed complexes in DMF coincide to electrolytic nature of the mixed complexes, consequently, these complexes could be subedited as [M(TMAP)(8Q)(H2O)]nX.yH2O (M=Co(II) and Cu(II) complexes(where n = 1, y = 0 ); [M(TMAP)(8Q)(H2O)]nX.yH2O (M = (where n = 1, y = 1 for Ni(

RKASFH Ghanim, Ibn Al -Haitham Journal for pure and applied science, 2017

The adsorption process of reactive blue 49 (RB49) dye and reactive red 195 (RR195) dye from an aqueous solutions was explored using a novel adsorbent produced from the sunflower husks encapsulated with copper oxide nanoparticle (CSFH). Primarily, the features of a CSFH, such as surface morphology, functional groups, and structure, were characterized. It was determined that coating the sunflower husks with copper oxide nanoparticles greatly improved the surface and structural properties related to the adsorption capacity. The adsorption process was successful, with a removal efficiency of 97% for RB49 and 98% for RR195 under optimal operating conditions, contact time of 180 min, pH of 7, agitation speed of 150 rpm, initial dye concentration

The objective of this study is to apply Artificial Neural Network for heat transfer analysis of shell-and-tube heat exchangers widely used in power plants and refineries. Practical data was obtained by using industrial heat exchanger operating in power generation department of Dura refinery. The commonly used Back Propagation (BP) algorithm was used to train and test networks by divided the data to three samples (training, validation and testing data) to give more approach data with actual case. Inputs of the neural network include inlet water temperature, inlet air temperature and mass flow rate of air. Two outputs (exit water temperature to cooling tower and exit air temperature to second stage of air compressor) were taken in ANN.

A resume is the first impression between you and a potential employer. Therefore, the importance of a resume can never be underestimated. Selecting the right candidates for a job within a company can be a daunting task for recruiters when they have to review hundreds of resumes. To reduce time and effort, we can use NLTK and Natural Language Processing (NLP) techniques to extract essential data from a resume. NLTK is a free, open source, community-driven project and the leading platform for building Python programs to work with human language data. To select the best resume according to the company’s requirements, an algorithm such as KNN is used. To be selected from hundreds of resumes, your resume must be one of the best. Theref

Reliability analysis methods are used to evaluate the safety of reinforced concrete structures by evaluating the limit state function 𝑔(𝑋𝑖). For implicit limit state function and nonlinear analysis , an advanced reliability analysis methods are needed. Monte Carlo simulation (MCS) can be used in this case however, as the number of input variables increases, the time required for MCS also increases, making it a time consuming method especially for complex problems with implicit performance functions. In such cases, MCS-based FORM (First Order Reliability Method) and Artificial Neural Network-based FORM (ANN FORM) have been proposed as alternatives. However, it is important to note that both MCS-FORM and ANN-FORM can also be time-con

The rise of edge-cloud continuum computing is a result of the growing significance of edge computing, which has become a complementary or substitute option for traditional cloud services. The convergence of networking and computers presents a notable challenge due to their distinct historical development. Task scheduling is a major challenge in the context of edge-cloud continuum computing. The selection of the execution location of tasks, is crucial in meeting the quality-of-service (QoS) requirements of applications. An efficient scheduling strategy for distributing workloads among virtual machines in the edge-cloud continuum data center is mandatory to ensure the fulfilment of QoS requirements for both customer and service provider. E