Abstract— The optimized methods for a forecasting demand have been discussed among researchers to overview the most sufficient need in a rapid expansion city. The methods developed test and compare between all interrelated issues. Many variables are involved in the calculation of energy future demand, the prediction makes aggressive result to achieved requirements. After discussing proposed research methods a suitable and accurate approach is elected to be applied for any city to satisfy the public demand. The objectives of this research are to overview all the methods of forecasting of the electrical demand using different approaches , and come out with the most optimum suitable procedure in the field of electrical network for long and short terms. The result indicates a brilliant analysis with high contribution in the electrical industry, will find its application in a global rapid expansion city. The methods show a tradeoff between cost, reliability and complexity of operations regardless of energy generated.

Structural changes affecting cardiomyocyte function may contribute to the pathophysiological

remodeling underlying cardiac function impairment. Recent reports have shown that

endogenous nitric oxide (NO) plays an important role in this process. In order to examine the role

of NO in cardiomyocyte remodeling, male rats were acclimated to room temperature (22± 1
C) or

cold (4 ± 1
C) and treated with 2.25% L-arginine
HCl or 0.01% L-NAME (Nx-nitro-L-arginine

methyl ester)
HCl for 45 days. Untreated groups served as controls. Right heart ventricles were routinely

prepared for light microscopic examination. Stereological estimations of volume densities of

cardiomyocytes, surrounding blood vessels and connective tissue, as well as the morphometric measurements

of cardiomyocyte diameters were performed. Tissue sections were also analyzed for

structural alterations. We observed that both L-arginine and L-NAME supplementation induced

cardiomyocyte hypertrophy, regardless of ambient temperature. However, cardiomyocyte hypertrophy

was associated with fibrosis and extra collagen deposition only in the L-NAME treated group.

Taken together, our results suggest that NO has a modulatory role in right heart ventricle remodeling

by coordinating hypertrophy of cardiomyocytes and fibrous tissue preventing cardiac fibrosis.

The segmentation of various retinal layers is vital for diagnosing and tracking progress of medication of various ocular diseases. Due to the complexity of retinal structures, the tediousness of manual segmentation and variation from different specialists, many methods have been proposed to aid with this analysis. However image artifacts, in addition to inhomogeneity in pathological structures, remain a challenge, with negative influence on the performance of segmentation algorithms. Previous attempts normally pre-process the images or model the segmentation to handle the obstruction but it still remains an area of active research, especially in relation to the graph based algorithms. In this paper we present an automatic retinal layer segmentation method, which is comprised of fuzzy histogram hyperbolization and graph cut methods to segment 8 boundaries and 7 layers of the retina on 150 OCT B-Sans images, 50 each from the temporal, nasal and centre of foveal region. Our method shows positive results, with additional tolerance and adaptability to contour variance and pathological inconsistency of the retinal structures in all regions.

In the medical field, the identification of skin cancer (Malignant Melanoma) in dermoscopy images is still a challenging task for radiologists and researchers. Due to its rapid increase, the need for decision support systems to assist the radiologists to detect it in early stages becomes essential and necessary. Computer Aided Diagnosis (CAD) systems have significant potential to increase the accuracy of its early detection. Typically, CAD systems use various types of features to characterize skin lesions. The features are often concatenated into one vector (early fusion) to represent the image. In this paper, we present a novel method for melanoma detection from images. First the lesions are segmented by combining Particle Swarm Optimization and Markov Random Field methods. Then the K-means is applied on the segmented lesions to separate them into homogeneous clusters, from which important features are extracted. Finally, an Artificial Neural Network with Radial Basis Function is applied for the detection of melanoma. The method was tested on 200 dermoscopy images. The experimental results show that the proposed method achieved higher accuracy in terms of melanoma detection, compared to alternative methods.