The current  research was ataxonomic  study for the species Pyrus pyraster L.  that belong to the subfamily Pomoideae from the family Rosaceae which growing wildely in Iraqi Kurdistan .The macro and micro morphology and pollen grain were studied .A wide field servay for the districts of Iraqi Kurdistan was done . According to the taxonomic references adetail study for the morphology of the leaves ,color of the corolla,calyx ,  the fruits diameters,  color and shapes,also the seed were studied  From the study of pollen grains it appeared that the pollen grains areTricolporate ,their shape in the Equatorial view was ovoid but in the pollar view they are triangular. The species Pyrus pyraster L.was recor

New complexes of Cu (ll), Ni (ll), Co (ll), and Zn (ll) wi th 2-amino-5-p-Fiouro Phenyl 1, 3, 4-Thiadiazole have been synthesized. The products were isolated, studied and characterized by physical measurements, ie,(Ff-IR), UV-Vis and the melting points were determined. The new Schiff base (L) has been used to prepare some complexes. The prepared complexes were identified and their structural geometry were suggested

The introduction of concrete damage plasticity material models has significantly improved the accuracy with which the concrete structural elements can be predicted in terms of their structural response. Research into this method's accuracy in analyzing complex concrete forms has been limited. A damage model combined with a plasticity model, based on continuum damage mechanics, is recommended for effectively predicting and simulating concrete behaviour. The damage parameters, such as compressive and tensile damages, can be defined to simulate concrete behavior in a damaged-plasticity model accurately. This research aims to propose an analytical model for assessing concrete compressive damage based on stiffness deterioration. The prop

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

New thermally stable aromatic poly(amide-imide)s ( PAI1- PAI4 ) were synthesized from direct polycondensation reaction of Terephthalic acid and Phthalic acid with two new different diamine monomers derivatives of 1,2,4,5-tetracarboxilic benzene dianhydride as a second diacides in a medium consisting of triphenyl phosphite (TPP) in N-methyl-2pyrrolidone (NMP) / pyridine solution containing dissolved calcium chloride CaCl2. The polymerization reaction produced a series of novel poly(amide-imide) in high yield. The new monomers were characterized by FTIR, 1H-NMR spectroscopy. The resulting polymers were typically characterized by means of FT-IR, 1H-NMR spectroscopy, and solubility tests. Thermal properties of the poly(amide-imide)s were als

 Abstract

In this paper, fatigue damage accumulation were studied using many methods i.e.Corton-Dalon (CD),Corton-Dalon-Marsh(CDM), new non-linear model and experimental method. The prediction of fatigue lifetimes based on the two classical methods, Corton-Dalon (CD)andCorton-Dalon-Marsh (CDM), are uneconomic and non-conservative respectively. However satisfactory predictions were obtained by applying the proposed non-linear model (present model) for medium carbon steel compared with experimental work. Many shortcomings of the two classical methods are related to their inability to take into account the surface treatment effect as shot peening. It is clear that the new model shows that a much better and cons

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Hydro cracking of heavy oil is used in refinery to produce invaluable products. In this research, a model of hydro cracking reactor has been used to study the behavior of heavy oil in hydro cracking under the conditions recommended by literature in terms lumping of feed and products. The lumping scheme is based on five lumps include: heavy oil, vacuum oil, distillates, naphtha and gases. The first order kinetics was assumed for the conversion in the model and the system is modeled as an isothermal tubular reactor. MATLAB 6.1 was used to solve the model for a five lump scheme for different values of feed velocity, and temperature.