This study was performed to investigate differences in the nasal profile of Iraqi adults with different skeletal class groups. Materials and methods: Cephalometric radiographs of 90 subjects of Iraqi origin, with the age range of 18-25 years. The collected radiographs were divided into three groups (n = 30) according to the skeletal discrepancies. Different lines and angles were measured and analyzed to determine the size, shape, and position of the nose relative to other facial structures from lateral cephalometric radiograph of each subject. Results: A number of statistically significant changes were found between cephalometric measurements that reflected differences between the three skeletal class groups. Significant male-female differences were found in the measurements of nasolabial angle and the horizontal distances relating the nose to the incisal edge of the most prominent maxillary central incisor and to the chin. The angular measurements of the nasal tip projection angle, nasomental angle and nasofacial angle were also considerably varied among the three skeletal class groups alongside the vertical distances relating the nose to the upper lip, the incisal edge of the most prominent maxillary central incisor and to the chin. Conclusion: This study adds valuable information about the impact of the size, shape of nose and its relative position to other craniofacial structures on the nasal profile in patients of Iraqi origin with different skeletal classes. Therefore, the results of the present study are useful guidance for cosmetic surgeons and orthodontists during diagnosis and planning for cosmetic rhinoplasty and orthodontic treatment in Iraqi adults.

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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 minimize the total costs, Where the approved model was able to minimize the total costs by 25%. A future study investigating optimization heuristic with stochastics demand would be very interesting.

In recent years, Bitcoin has become the most widely used blockchain platform in business and finance. The goal of this work is to find a viable prediction model that incorporates and perhaps improves on a combination of available models. Among the techniques utilized in this paper are exponential smoothing, ARIMA, artificial neural networks (ANNs) models, and prediction combination models. The study's most obvious discovery is that artificial intelligence models improve the results of compound prediction models. The second key discovery was that a strong combination forecasting model that responds to the multiple fluctuations that occur in the bitcoin time series and Error improvement should be used. Based on the results, the prediction accuracy criterion and matching curve-fitting in this work demonstrated that if the residuals of the revised model are white noise, the forecasts are unbiased. Future work investigating robust hybrid model forecasting using fuzzy neural networks would be very interesting.