Finding a path solution in a dynamic environment represents a challenge for the robotics researchers, furthermore, it is the main issue for autonomous robots and manipulators since nowadays the world is looking forward to this challenge. The collision free path for robot in an environment with moving obstacles such as different objects, humans, animals or other robots is considered as an actual problem that needs to be solved.  In addition, the local minima and sharp edges are the most common problems in all path planning algorithms. The main objective of this work is to overcome these problems by demonstrating the robot path planning and obstacle avoidance using D star (D*) algorithm based on Particle Swarm Optimization (PSO)

Volumetric Modulated Arc Therapy (VMAT) and Intensity Modulated Radiation Therapy (IMRT) are comparable for nasopharyngeal cancerous radiation therapy. This research intends to analyze the high-quality plan using accomplishment, conformance, and homogeneity criteria.

The study involved 40 patients with a postnasal cancerous tumor. The patients underwent computed tomography (CT) simulation to scan the anatomical details of the patients' heads. Then, their data was forwarded to the treatment planning system (TPS) workstation for IMRT and VMAT planning. The plans were evaluated using the IOA, HI, and CI indices.

The nasopharynx coverage results consist of the GTV and PTV at 95%. The statistical study reveals that VMAT provides

The Research aims to investigate into reality in terms of planning and scheduling management process for sake the implementation and maintenance of irrigation and drainage projects in the Republic of Iraq, with an indication of the most important obstacles that impede the planning and scheduling management process for these projects and ways of addressing them and minimizing their effects.                                                  For the purpose of achieving the goal of the research, a sci

This research aims to know the reality of succession planning in the researched organization and how it occupies the important jobs in the organization and its readiness for sudden and urgent events that lead to the vacancy of these jobs, and the importance of this research is that it is one of the rare Arab and Iraqi studies and research in this field, as it is one of the first researches It deals with the reality of succession planning in light of the retirement law No. (26) of 2019, and the research problem was the absence of consecutive planning programs and the weak preparation of suitable candidates to face sudden and urgent conditions as replacements for current leaders who occupy the leading and key positions in the depar

     Tow simple, rapid and sensitive spectrophotometric methods for the determination of mesalazine in pharmaceutical preparations have been carried out. The proposed methods depend on oxidative coupling reaction of mesalazine with m-aminophenol in the existence of N-bromosuccinamide in alkaline medium (method A) and 2,6-dihydroxybenzoic acid in the existence of sodium metaperiodate in basic medium (method B) to produce colored products , show highest absorptions at 640 (nm) and 515 (nm), alternately. Beer’s law was consistent in concentrations extent of 1.25-30 and 0.5-12.5 (µg.mL^-1) with molar absorptivity of 0.36×10⁴ and 0.77×10⁴ L.mol^-1.cm<

We use of multi-choice Goal Programming (MCGP), which is a developed model of Goal Programming where it is used in circumstances of the multiplicity and difference of goals when choosing between decision alternatives in cases of allocating resources, as it is a model that seeks to find the closest and best solutions to the specific values ​​of the goals within the aspiration levels, as the first goal in the multi-choice goal programming model that is used to reduce the total cost of storage and shortage, while the other goal was to reduce the difference between the real demand that the hospitals need from the blood transfusion center and the units that already achieved. The case Iraqi Center

Deep learning has recently received a lot of attention as a feasible solution to a variety of artificial intelligence difficulties. Convolutional neural networks (CNNs) outperform other deep learning architectures in the application of object identification and recognition when compared to other machine learning methods. Speech recognition, pattern analysis, and image identification, all benefit from deep neural networks. When performing image operations on noisy images, such as fog removal or low light enhancement, image processing methods such as filtering or image enhancement are required. The study shows the effect of using Multi-scale deep learning Context Aggregation Network CAN on Bilateral Filtering Approximation (BFA) for d