Researching the effects of the research and technological development contract, determining its extent and demarcating the boundaries of the obligations imposed in it, is the cornerstone of economic growth and development, because defining these obligations removes the ambiguity and conflict between interests, by stating the rights owed to each party and even trying to reconcile them, or impose protection by specifying guarantees that are compatible with the essence of the R&D contract, For the purpose of studying the subject thoroughly, we will divide this research into two sections. The first is devoted to identifying the parties to the research and technological development contract. As for the other topic, we will explain the obligation

Roughness length is one of the key variables in micrometeorological studies and environmental studies in regards to describing development of cities and urban environments. By utilizing the three dimensions ultrasonic anemometer installed at Mustansiriyah university, we determined the rate of the height of the rough elements (trees, buildings and bridges) to the surrounding area of the university for a radius of 1 km. After this, we calculated the zero-plane displacement length of eight sections and calculated the length of surface roughness. The results proved that the ranges of the variables above are Z_H (9.2-13.8) m, Z_d (4.3-8.1) m and Z_o (0.24-0.48) m.

The importance of this research has been to rationalize the cost of producing maize seeds through the followers of modern techniques and methods in agricultural activities such as genetic engineering for increasing production efficiency of maize seeds as well as the importance of calculating seed cost rationalization through the ABC system and thus rationalizing government spending. The research is based on one hypothesis in two ways that the use of genetic engineering on maize seeds works to: one - increase production efficiency of seeds and savings in agricultural inputs. 2. Rationalize the costs of examining and planting maize seeds. In order to calculate the costs will be based on the cost system based on activities ABC. The research

Background: Maxillary first premolar with wide MOD cavity more susceptible to fracture. The aim of this study was to assess the influence of cavity design for cusp coverage on the fracture resistance of weakened maxillary first premolar restored with CAD/CAM hybrid ceramic versus nanohybide composite. Materials and Methods: Fifty six intact maxillary first premolars of approximately comparable sizes were divided into seven groups eight for each: Group A: Intact teeth (control group); Group B: teeth prepared for MOD inlay; Group C: teeth prepared for MOD onlay covering the lingual cusp; Group D: teeth prepared for MOD covering buccal and lingual cusps ,the previous three groups indirectly restored with nanohybrid composite (3M ESPE Z 250 X

study was conducted on a stretch of Tigris river crossing Baghdad city to determine the concentration of some chlorophenols pollutants. Aqueous samples were preliminary enriched about 500 times and the chlorophenols have determined using high performance liquid chromatography HPLC. Limits of detection LOD were (0.007–0.012 mg L-1), relative standard deviations RSD% were 2.4%–5.59% and relative recoveries were 51.06%– 104.07%. The existence of chlorophenols in Tigris river was in the range 0.023–4.596 mg L-1. The developed method suggested in this study can be applied for routine analysis and monitoring of chlorinated phenols in environmental aqueous samples.

Lung cancer is one of the most serious and prevalent diseases, causing many deaths each year. Though CT scan images are mostly used in the diagnosis of cancer, the assessment of scans is an error-prone and time-consuming task. Machine learning and AI-based models can identify and classify types of lung cancer quite accurately, which helps in the early-stage detection of lung cancer that can increase the survival rate. In this paper, Convolutional Neural Network is used to classify Adenocarcinoma, squamous cell carcinoma and normal case CT scan images from the Chest CT Scan Images Dataset using different combinations of hidden layers and parameters in CNN models. The proposed model was trained on 1000 CT Scan Images of cancerous and non-c