The integration of decision-making will lead to the robust of its decisions, and then determination optimum inventory level to the required materials to produce and reduce the total cost by the cooperation of purchasing department with inventory department and also with other company^,s departments. Two models are suggested to determine Optimum Inventory Level (OIL), the first model (OIL-model 1) assumed that the inventory level for materials quantities equal to the required materials, while the second model (OIL-model 2) assumed that the inventory level for materials quantities more than the required materials for the next period.             &nb

Maximizing the net present value (NPV) of oil field development is heavily dependent on optimizing well placement. The traditional approach entails the use of expert intuition to design well configurations and locations, followed by economic analysis and reservoir simulation to determine the most effective plan. However, this approach often proves inadequate due to the complexity and nonlinearity of reservoirs. In recent years, computational techniques have been developed to optimize well placement by defining decision variables (such as well coordinates), objective functions (such as NPV or cumulative oil production), and constraints. This paper presents a study on the use of genetic algorithms for well placement optimization, a ty

Abstract: Word sense disambiguation (WSD) is a significant field in computational linguistics as it is indispensable for many language understanding applications. Automatic processing of documents is made difficult because of the fact that many of the terms it contain ambiguous. Word Sense Disambiguation (WSD) systems try to solve these ambiguities and find the correct meaning. Genetic algorithms can be active to resolve this problem since they have been effectively applied for many optimization problems. In this paper, genetic algorithms proposed to solve the word sense disambiguation problem that can automatically select the intended meaning of a word in context without any additional resource. The proposed algorithm is evaluated on a col

As s widely use of exchanging private information in various communication applications, the issue to secure it became top urgent. In this research, a new approach to encrypt text message based on genetic algorithm operators has been proposed. The proposed approach follows a new algorithm of generating 8 bit chromosome to encrypt plain text after selecting randomly crossover point. The resulted child code is flipped by one bit using mutation operation. Two simulations are conducted to evaluate the performance of the proposed approach including execution time of encryption/decryption and throughput computations. Simulations results prove the robustness of the proposed approach to produce better performance for all evaluation metrics with res

     In this paper, the botnet detection problem is defined as a feature selection problem and the genetic algorithm (GA) is used to search for the best significant combination of features from the entire search space of set of features. Furthermore, the Decision Tree (DT) classifier is used as an objective function to direct the ability of the proposed GA to locate the combination of features that can correctly classify the activities into normal traffics and botnet attacks. Two datasets  namely the UNSW-NB15 and the Canadian Institute for Cybersecurity Intrusion Detection System 2017 (CICIDS2017), are used as evaluation datasets. The results reveal that the proposed DT-aware GA can effectively find the relevant features from

: Cigarette smoking is a lifestyle behavior that causes significant adverse health effects. Cigarette smoke contains chemicals, many of which are lead to the production of reactive oxygen species (ROS), which can lead to apoptosis and autophagy. To estimate the association of Cigarette smoking with the autophagy and immunity, technology of real time polymerase chain reaction (RTPCR) for gene expression of (LC3A, LC3B, LC3C, myd88) was used. Enzyme-linked immunosorbent assay (ELISA) technique was utilized to measurement the amount of TNF-α protein. The ratios of LC3A/LC3B and LC3B/LC3C were calculated to estimate the autophagy flux. The results indicate the expression of LC3B, LC3C and Myd88 genes in smokers is increased significantly (p

Background: Acute myeloid leukemia (AML) is a genetically heterogeneous leukemia characterized by abnormal myeloid blast accumulation, disrupting normal hematopoiesis and leading to rapid progression. Objective: To investigate SNPs within the 3’UTR of the CCAAT/enhancer-binding protein alpha (CEBPA) gene and its association with AML in Iraqi patients. Methods: The study was carried out on 120 AML patients classified into newly diagnosed, induction chemotherapy, and consolidation chemotherapy stages (40 each), and 40 individuals as a control group. Genomic DNA was extracted from AML patients and controls, followed by PCR amplification and Sanger sequencing of the 3’UTR region of the CEBPA gene. The AML patients were characterized