KE Sharquie, AA Noaimi, E Abdulqader, WK Al-Janabi, J Dermatol Venereol, 2020 - Cited by 6

What distinguishes the athlete in dealing with all stimuli is the ability to understand the cognitive rules through which he acts and directs behavior through thinking and regular planning methods in dealing with the environment in a realistic manner, and this comes through techniques and means based on modernity in obtaining information that makes the athlete arrange in His memory is the programs that are the most important crutch for relying on when he asks for them in applying and executing the skill assignment. One of the enhancers of awareness of variables is the ability of coaches to provide openness in modern ideas to find solutions, through which the player can sense and interpret events and produce outputs for quick and successful

Several toxigenic cyanobacteria produce the cyanotoxin (microcystin). Being a health and environmental hazard, screening of water sources for the presence of microcystin is increasingly becoming a recommended environmental procedure in many countries of the world. This study was conducted to assess the ability of freshwater cyanobacterial species Westiellopsis prolifica to produce microcystins in Iraqi freshwaters via using molecular and immunological tools. The toxigenicity of W. prolifica was compared via laboratory experiments with other dominant bloom-forming cyanobacteria isolated from the Tigris River: Microcystis aeruginosa, Chroococcus turigidus, Nostoc carneum, and Lyngbya sp. signifi

With the World Health Organization reporting over 30,000 deaths and 200,000 to 400,000 new cases annually, visceral leishmaniasis is a serious disease affecting some of the world's poorest people. As drug resistance continues to rise, there is a huge unmet need to improve treatment. Miltefosine remains one of the main treatments for leishmaniasis, yet its mode of action (MoA) is still unknown. Understanding the MoA of this drug and parasite response to treatment could help pave the way for new and more successful treatments for leishmaniasis. A novel method has been devised to study the metabolome and lipidome ofLeishmania donovaniaxenic amastigotes treated with miltefosi

According to the importance of the conveyor systems in various industrial and service lines, it is very desirable to make these systems as efficient as possible in their work. In this paper, the speed of a conveyor belt (which is in our study a part of an integrated training robotic system) is controlled using one of the artificial intelligence methods, which is the Artificial Neural Network (ANN). A visions sensor will be responsible for gathering information about the status of the conveyor belt and parts over it, where, according to this information, an intelligent decision about the belt speed will be taken by the ANN controller. ANN will control the alteration in speed in a way that gives the optimized energy efficiency through

Shadow detection and removal is an important task when dealing with color outdoor images. Shadows are generated by a local and relative absence of light. Shadows are, first of all, a local decrease in the amount of light that reaches a surface. Secondly, they are a local change in the amount of light rejected by a surface toward the observer. Most shadow detection and segmentation methods are based on image analysis. However, some factors will affect the detection result due to the complexity of the circumstances. In this paper a method of segmentation test present to detect shadows from an image and a function concept is used to remove the shadow from an image.

Neural cryptography deals with the problem of “key exchange” between two neural networks by using the mutual learning concept. The two networks exchange their outputs (in bits) and the key between two communicating parties ar eventually represented in the final learned weights, when the two networks are said to be synchronized. Security of neural synchronization is put at risk if an attacker is capable of synchronizing with any of the two parties during the training process.

Diabetes is one of the increasing chronic diseases, affecting millions of people around the earth. Diabetes diagnosis, its prediction, proper cure, and management are compulsory. Machine learning-based prediction techniques for diabetes data analysis can help in the early detection and prediction of the disease and its consequences such as hypo/hyperglycemia. In this paper, we explored the diabetes dataset collected from the medical records of one thousand Iraqi patients. We applied three classifiers, the multilayer perceptron, the KNN and the Random Forest. We involved two experiments: the first experiment used all 12 features of the dataset. The Random Forest outperforms others with 98.8% accuracy. The second experiment used only five att