This paper presents the synthesis and study of some new mixed-liagnd complexes containing nicotinamide(C6H7N2O) symbolized (NA) and phenylalanine (C9H11NO2)symbolized (pheH)] with some metal ions. The resulting products were found to be solid crystalline complexes which have been characterized by :Melting points, Solubility, Molar conductivity. determination the percentage of the metal in the complexes by flame(AAS), magnetic susceptipibility, Spectroscopic Method [FT-IR and UV-Vis]. The proposed structure of the complexes using program , chem office 3D(2006) . The general formula have been given for the prepared complexes :[M(NA)2(phe)]cl M(II): Mn(II) ,Co(II) , Ni(II) , Cu(II) , Zn(II) , Cd(II) & Hg(II) . NA = Nicotinamide= C6H7N2O Phe -

water quality assessment is still being done at specific locations of major concern. The use of Geographical Information System (GIS) based water quality information system and spatial analysis with Inverse Distance Weighted interpolation enabled the mapping of water quality indicators along Tigris river in Salah Al-Din government, Iraq. Water quality indicators were monitored by taking 13 river samples from different locations along the river during Winter season year 2020. Maps of 10 water quality indicators. This meant that the specific water quality indicator and diffuse pollution characteristics in the basin were better illustrated with the variations displayed along the course of the river than conventional line graphs. Creation of

Spatial data observed on a group of areal units is common in scientific applications. The usual hierarchical approach for modeling this kind of dataset is to introduce a spatial random effect with an autoregressive prior. However, the usual Markov chain Monte Carlo scheme for this hierarchical framework requires the spatial effects to be sampled from their full conditional posteriors one-by-one resulting in poor mixing. More importantly, it makes the model computationally inefficient for datasets with large number of units. In this article, we propose a Bayesian approach that uses the spectral structure of the adjacency to construct a low-rank expansion for modeling spatial dependence. We propose a pair of computationally efficient estimati

دُرِست العوامل المؤثرة في عدد ساعات تجهيز الكهرباء في مدينة بغداد، وتكونت عينة الدراسة من (365) مشاهدة يومية لعام 2018، وتمثلت بستة متغيرات استعملت في الدراسة. كان الهدف الرئيس هو دراسة العلاقة بين هذه المتغيرات، وتقدير تأثيرات المتغيرات التنبؤية في المتغير التابع (عدد ساعات تجهيز الكهرباء في مدينة بغداد). ولتحقيق ذلك استعملت نمذجة المعادلات الهيكلية/ تحليل المسار وبرنامج AMOS

The purpose of the study is the city of Baghdad, the capital of Iraq, was chosen to study the spectral reflection of the land cover and to determine the changes taking place in the areas of the main features of the city using the temporal resolution of multispectral bands of the satellite Landsat 5 and 8 for MSS and OLI sensors respectively belonging to NASA and for the period 1999-2021, and calculating the increase and decrease in the basic features of Baghdad. The main conclusions of the study were, This study from 1999 to 2021 and in two different seasons: the Spring of the growing season and Summer the dry season. When using the supervised classification method to determine the differences, the results showed remarkable changes. Where h

The segmentation of aerial images using different clustering techniques offers valuable insights into interpreting and analyzing such images. By partitioning the images into meaningful regions, clustering techniques help identify and differentiate various objects and areas of interest, facilitating various applications, including urban planning, environmental monitoring, and disaster management. This paper aims to segment color aerial images to provide a means of organizing and understanding the visual information contained within the image for various applications and research purposes. It is also important to look into and compare the basic workings of three popular clustering algorithms: K-Medoids, Fuzzy C-Mean (FCM), and Gaussia