The isolates were screened according to their capability for pectinase production, screening process identified the best pectinolytic isolate and it was characterized by cultural and biochemical, as Pesudomonas sp. Pectinolytic enzyme producing bacterium Pesudomonas sp. was isolated from the Iraqi soil on nutrient agar plate. Optimiztion of process parameters were carried out by altering some of environmental conditions of chemo-physical environment for the production medium. The highest pectinase production was observed at 48 hrs of incubation at 35 °C with the initial pH of 6.0. Different nutrients and environmental conditions were investigated in terms of their effect on the production of extracellular pectinase using citrus pectin a

  External Morphological study of  species Saprinus discoidalis of subfamily saprininae from family Histeridae of the order coleopteran. The specements of  the present study were collected from Salahdin Governor-ate (Beigi &Balad) and Governorate during May 2012.         The present study included detailed description of most body parts (Head, Thorax, Abdomen and their appendages) identification and recognition of species study male genitalia. The species Saprinus discoidalis LeConte is a new record to Iraqi fauna of Coleoptera.
 

 The technology in continuous and quick development, that reflects in all parts of our life and interred both scientific and practical fields. Marketing is one of them, a customer’s way to deal with choosing and demanding the product deferent from the traditional way. Some of the buying processes are electronic now, therefore the current research is identifying the digital channels that entered the world of marketing and influenced the activities and types that fall under this name and how it affects in positioning strategy, which is how to install the product or brand in the mind of the customer and was dimensions (brand identity, brand personality, brand communication, brand awareness, brand image), The researcher took t

Researchers are increasingly using multimodal biometrics to strengthen the security of biometric applications. In this study, a strong multimodal human identification model was developed to address the growing problem of spoofing attacks in biometric security systems. Through the use of metaheuristic optimization methods, such as the Genetic Algorithm(GA), Ant Colony Optimization(ACO), and Particle Swarm Optimization (PSO) for feature selection, this unique model incorporates three biometric modalities: face, iris, and fingerprint. Image pre-processing, feature extraction, critical image feature selection, and multibiometric recognition are the four main steps in the workflow of the system. To determine its performance, the model wa

Among a variety of approaches introduced in the literature to establish duality theory, Fenchel duality was of great importance in convex analysis and optimization. In this paper we establish some conditions to obtain classical strong Fenchel duality for evenly convex optimization problems defined in infinite dimensional spaces. The objective function of the primal problem is a family of (possible) infinite even convex functions. The strong duality conditions we present are based on the consideration of the epigraphs of the c-conjugate of the dual objective functions and the ε-c-subdifferential of the primal objective functions.

The purpose of this paper is to solve the stochastic demand for the unbalanced transport problem using heuristic algorithms to obtain the optimum solution, by minimizing the costs of transporting the gasoline product for the Oil Products Distribution Company of the Iraqi Ministry of Oil. The most important conclusions that were reached are the results prove the possibility of solving the random transportation problem when the demand is uncertain by the stochastic programming model. The most obvious finding to emerge from this work is that the genetic algorithm was able to address the problems of unbalanced transport, And the possibility of applying the model approved by the oil products distribution company in the Iraqi Ministry of Oil to m

This paper proposes a novel meta-heuristic optimization algorithm called the fine-tuning meta-heuristic algorithm (FTMA) for solving global optimization problems. In this algorithm, the solutions are fine-tuned using the fundamental steps in meta-heuristic optimization, namely, exploration, exploitation, and randomization, in such a way that if one step improves the solution, then it is unnecessary to execute the remaining steps. The performance of the proposed FTMA has been compared with that of five other optimization algorithms over ten benchmark test functions. Nine of them are well-known and already exist in the literature, while the tenth one is proposed by the authors and introduced in this article. One test trial was shown t