Autorías: Ismael Saleem Abed, Imad Kadhim Khlaif, Salah Mahmood Salman. Localización: Revista iberoamericana de psicología del ejercicio y el deporte. Nº. 5, 2022. Artículo de Revista en Dialnet.

Abstract
  financial market occupy very important place in the economic activity all over the world countris, and its importance increased with considerable technological progress in the world of transportation ,communications and information where its impact have spread over the whole world, which led to link the international economy in a kind of international relations so that the open policy became the prevailing trend in national and regional economies within the framework of the new world order.

the international economy has faced the financial crisis, global, that hit all world economies although the United States is the center of the crisis and the starting spark for it w

The research aims to shed light on the obstacles that hinder the use of taxpayers' commercial books covered by provisions of the amended commercial book-keeping system No. 2 of 1985 and ways to address these obstacles. On this basis, the main null hypothesis was formulated that there is no statistically significant relationship between the obstacles to the adoption of the commercial books and taxable income.

The research data were collected on the base of three applied case studies of registered taxpayers in the General Commission of Taxes, GCT, together with the use of a questionnaire distributed to a sample of taxpayers ( companies ). The collected data were analyzed, the result  were presented  and the hypothesis was

Shadow removal is crucial for robot and machine vision as the accuracy of object detection is greatly influenced by the uncertainty and ambiguity of the visual scene. In this paper, we introduce a new algorithm for shadow detection and removal based on different shapes, orientations, and spatial extents of Gaussian equations. Here, the contrast information of the visual scene is utilized for shadow detection and removal through five consecutive processing stages. In the first stage, contrast filtering is performed to obtain the contrast information of the image. The second stage involves a normalization process that suppresses noise and generates a balanced intensity at a specific position compared to the neighboring intensit

Image of landsate-7 taken by thematic mapper was used and classified using supervised method. Results of supervised classification indicated presence of nine land cover classes. Salt-soils class shows the highest reflectance value while water bodies' class shows the lowest values. Also the results indicated that soil properties show different effects on reflectance. There was a high significant positive relation of carbonate, gypsum, electric conductivity and silt content, while there was a week positive relation with sand and negative relation with organic matter, water content, bulk density and cataion exchange capacity.

Image of landsate-7 taken by thematic mapper was used and classified using supervised method. Results of supervised classification indicated presence of nine land cover classes. Salt-soils class shows the highest reflectance value while water bodies' class shows the lowest values. Also the results indicated that soil properties show different effects on reflectance. There was a high significant positive relation of carbonate, gypsum, electric conductivity and silt content, while there was a week positive relation with sand and negative relation with organic matter, water content, bulk density and cataion exchange capacity.

Cassava, a significant crop in Africa, Asia, and South America, is a staple food for millions. However, classifying cassava species using conventional color, texture, and shape features is inefficient, as cassava leaves exhibit similarities across different types, including toxic and non-toxic varieties. This research aims to overcome the limitations of traditional classification methods by employing deep learning techniques with pre-trained AlexNet as the feature extractor to accurately classify four types of cassava: Gajah, Manggu, Kapok, and Beracun. The dataset was collected from local farms in Lamongan Indonesia. To collect images with agricultural research experts, the dataset consists of 1,400 images, and each type of cassava has