Earth’s climate changes rapidly due to the increases in human demands and rapid economic growth. These changes will affect the entire biosphere, mostly in negative ways. Predicting future changes will put us in a better position to minimize their catastrophic effects and to understand how humans can cope with the new changes beforehand. In this research, previous global climate data set observations from 1961-1990 have been used to predict the future climate change scenario for 2010-2039. The data were processed with Idrisi Andes software and the final Köppen-Geiger map was created with ArcGIS software. Based on Köppen climate classification, it was found that areas of Equator, Arid Steppes, and Snow will decrease by 3.9 %, 2.96%, and 0.09%, respectively. While the areas of Warm Temperature and Dessert will increase by 4.5% and 0.75%, respectively. The results of this study provide useful information on future climate Köppen-Geiger maps and areas that will most likely be affected by climate change in the following decades
On of the direct causes which led to the global financial crisis 2008 is decrease or collapse in liquidity of large financial institutions which is reflected on investments of a considerable number of institutions and persons.
This study aim's through out its three sections to explain the disclosure level of financial institutions which affected by Financial Crisis from liquidity information which explained in the statement of cash flow according to Timeliness and Completeness.
The study concluded an important result the company of research sample was disclosure in Timeliness and Completeness from all of accounting information is related in liquidity or that related in result of operations and financial position. The more
... Show MoreIn this paper, we used four classification methods to classify objects and compareamong these methods, these are K Nearest Neighbor's (KNN), Stochastic Gradient Descentlearning (SGD), Logistic Regression Algorithm(LR), and Multi-Layer Perceptron (MLP). Weused MCOCO dataset for classification and detection the objects, these dataset image wererandomly divided into training and testing datasets at a ratio of 7:3, respectively. In randomlyselect training and testing dataset images, converted the color images to the gray level, thenenhancement these gray images using the histogram equalization method, resize (20 x 20) fordataset image. Principal component analysis (PCA) was used for feature extraction, andfinally apply four classification metho
... Show MoreThis paper presents a method to classify colored textural images of skin tissues. Since medical images havehighly heterogeneity, the development of reliable skin-cancer detection process is difficult, and a mono fractaldimension is not sufficient to classify images of this nature. A multifractal-based feature vectors are suggested hereas an alternative and more effective tool. At the same time multiple color channels are used to get more descriptivefeatures.Two multifractal based set of features are suggested here. The first set measures the local roughness property, whilethe second set measure the local contrast property.A combination of all the extracted features from the three colormodels gives a highest classification accuracy with 99.4
... Show Moreيحتل النفط مكانة هامة وجوهرية ضمن اقتصاديات العالم، حيث إنه يمثل سلعة استراتيجية عالمية ومادة أولية أساسية في الصناعات الكيميائية والبتروكيميائية، كما أنه متعدد الاستعمالات، سواء في الصناعة أو الزراعة، بالإضافة إلى استعمالاته في الحياة اليومية للإنسان، مما جعله ذا مكانة مهمة ضمن أطر التجارة الدولية وموقع خاص في تشكيل الخريطة الاقتصادية للعالم. وقد جاء هذا البحث ليسلط الضوء على استراتيجيات منظمة الأوبك لض
... Show MoreBackground:Measurement of hemoglobin A1c (A1C) is a renowned tactic for gauging long-term glycemic control, and exemplifies an outstanding influence to the quality of care in diabetic patients.The concept of targets is open to criticism; they may be unattainable, or limit what could be attained, and in addition they may be economically difficult to attain. However, without some form of targeted control of an asymptomatic condition it becomes difficult to promote care at allObjectives: The present article aims to address the most recent evidence-based global guidelines of A1C targets intended for glycemic control in Type 2 Diabetes Mellitus (T2D).Key messages:Rationale for Treatment Targets of A1C includesevidence for microvascular and ma
... Show MoreCeliac disease (CD) is an immune-mediated disorder caused by gluten in genetically susceptible individuals characterized by chronic inflammation that essentially affects the small intestine. Objective: this study was designed to measure the potential role of some serological biomarkers including vitamin B12 and homocysteine (HCY) in the progression of CD as well as their relations to global DNA methylation (5mC). Materials and methods. Forty CD patients were enrolled in the study with an average age of (36.60 ± 2.03) years (range between 15 and 60). The diagnosis of the disease was confirmed by serological examinations and intestinal endoscopy in Gastroenterology and Liver Teaching Hospital in the Medical City Hospital in Baghdad
... Show MoreThis paper proposes a new approach, of Clustering Ultrasound images using the Hybrid Filter (CUHF) to determine the gender of the fetus in the early stages. The possible advantage of CUHF, a better result can be achieved when fuzzy c-mean FCM returns incorrect clusters. The proposed approach is conducted in two steps. Firstly, a preprocessing step to decrease the noise presented in ultrasound images by applying the filters: Local Binary Pattern (LBP), median, median and discrete wavelet (DWT),(median, DWT & LBP) and (median & Laplacian) ML. Secondly, implementing Fuzzy C-Mean (FCM) for clustering the resulted images from the first step. Amongst those filters, Median & Laplace has recorded a better accuracy. Our experimental evaluation on re
... Show MoreThis paper proposes a new approach, of Clustering Ultrasound images using the Hybrid Filter (CUHF) to determine the gender of the fetus in the early stages. The possible advantage of CUHF, a better result can be achieved when fuzzy c-mean FCM returns incorrect clusters. The proposed approach is conducted in two steps. Firstly, a preprocessing step to decrease the noise presented in ultrasound images by applying the filters: Local Binary Pattern (LBP), median, median and discrete wavelet (DWT), (median, DWT & LBP) and (median & Laplacian) ML. Secondly, implementing Fuzzy C-Mean (FCM) for clustering the resulted images from the first step. Amongst those filters, Median & Lap