The aim of this research is to construct a three-dimensional maritime transport model to transport nonhomogeneous goods (k) and different transport modes (v) from their sources (i) to their destinations (j), while limiting the optimum quantities v ijk x to be transported at the lowest possible cost v ijk c and time v ijk t using the heuristic algorithm, Transport problems have been widely studied in computer science and process research and are one of the main problems of transport problems that are usually used to reduce the cost or times of transport of goods with a number of sources and a number of destinations and by means of transport to meet the conditions of supply and demand. Transport models are a key tool in logistics an

The aim of the work is synthesis and characterization of bidentate ligand [3-(3-acetylphenylamino)-5,5-dimethylcyclohex-3-enone][HL], from the reaction of dimedone with 3-amino acetophenone to produce the ligand [HL], the reaction was carried out in dry benzene as a solvent under reflux. The prepared ligand [HL] was characterized by FT-IR, UV-Vis spectroscopy, 'H, 8C-NMR spectra, Mass spectra, (C.H.N) and melting point. The mixed ligand complexes were prepared from ligand [HL] was used as a primary ligand while 8-hydroxy quinoline [HQ] was used as a secondary ligand with metal ion M(IT).Where M(IT) = (Mn ,Co ,Ni ,Cu ,Zn ,Cd and Pd) at reflux ,using ethanol as a solvent, KOH as a

The aim of the work is synthesis and characterization of bidentate ligand [3-(3-acetylphenylamino)-5,5-dimethylcyclohex-3-enone][HL], from the reaction of dimedone with 3-amino acetophenone to produce the ligand [HL], the reaction was carried out in dry benzene as a solvent under reflux. The prepared ligand [HL] was characterized by FT-IR, UV-Vis spectroscopy, 1H, 13C-NMR spectra, Mass spectra, (C.H.N) and melting point. The mixed ligand complexes were prepared from ligand [HL] was used as a primary ligand while 8-hydroxy quinoline [HQ] was used as a secondary ligand with metal ion M(Π).Where M(Π) = (Mn ,Co ,Ni ,Cu ,Zn ,Cd and Pd) at reflux ,using ethanol as a solvent, KOH as a base. Complexes of the composition [M(L)(Q)] with (1

Complexes of Lanthanide ione Ln(III) =La(III) , Ce(III),Pr(III) and Nd(III) withligands of nicotinamide (na) and Benzimidazole (BIMD) have been prepared withgeneral formula [M(na)3(BIMD)3](NO3) where :M = Ln(III) = La(III) , Ce(III) , Gd(III) , Nd(III) .Na = nicotinamide = C7H6N2OBIMD = Benzimidazole = C7H6N2All compounds have been characterized by spectroscopic methods [FT-IR , UV-VIS ,AAS] , microanalysis (C.H.N) Along with conductivity measurements , solubility ,melting point , theroitical measurment by using chem office 3D prog .Model (2000) .Frome the above data the proposed moleculer structure for all complexes with its ionsis octahydral geometries

In this paper, image compression technique is presented based on the Zonal transform method. The DCT, Walsh, and Hadamard transform techniques are also implements. These different transforms are applied on SAR images using Different block size. The effects of implementing these different transforms are investigated. The main shortcoming associated with this radar imagery system is the presence of the speckle noise, which affected the compression results.

This paper presents the synthesis and study of some new mixed-ligand 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= C6

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 -

Abstract

       The grey system model GM(1,1) is the model of the prediction of the time series and the basis of the grey theory. This research presents the methods for estimating parameters of the grey model GM(1,1) is the accumulative method (ACC), the exponential method (EXP), modified exponential method (Mod EXP) and the Particle Swarm Optimization method (PSO). These methods were compared based on the Mean square error (MSE) and the Mean Absolute percentage error (MAPE) as a basis comparator and the simulation method was adopted for the best of the four methods, The best method was obtained and then applied to real data. This data represents the consumption rate of two types of oils a he

The aim of this paper to find Bayes estimator under new loss function assemble between symmetric and asymmetric loss functions, namely, proposed entropy loss function, where this function that merge between entropy loss function and the squared Log error Loss function, which is quite asymmetric in nature. then comparison a the Bayes estimators of exponential distribution under the proposed function, whoever, loss functions ingredient for the proposed function the using a standard mean square error (MSE) and Bias quantity (M_bias), where the generation of the random data using the simulation for estimate exponential distribution parameters different sample sizes (n=10,50,100) and (N=1000), taking initial

Big data analysis is essential for modern applications in areas such as healthcare, assistive technology, intelligent transportation, environment and climate monitoring. Traditional algorithms in data mining and machine learning do not scale well with data size. Mining and learning from big data need time and memory efficient techniques, albeit the cost of possible loss in accuracy. We have developed a data aggregation structure to summarize data with large number of instances and data generated from multiple data sources. Data are aggregated at multiple resolutions and resolution provides a trade-off between efficiency and accuracy. The structure is built once, updated incrementally, and serves as a common data input for multiple mining an