Hydrate dissociation equilibrium conditions for carbon dioxide + methane with water, nitrogen + methane with water and carbon dioxide + nitrogen with water were measured using cryogenic sapphire cell. Measurements were performed in the temperature range of 275.75 K–293.95 K and for pressures ranging from 5 MPa to 25 MPa. The resulting data indicate that as the carbon dioxide concentration is increased in the gas mixture, the gas hydrate equilibrium temperature increases. In contrast, by increasing the nitrogen concentration in the gas mixtures containing methane or carbon dioxide decreased the gas hydrate equilibrium temperatures. Furthermore, the cage occupancies for the carbon dioxide + methane system were evaluated using the Van der Wa

Deep drawing process to produce square cup is very complex process due to a lot of process parameters which control on this process, therefore associated with it many of defects such as earing, wrinkling and fracture. Study of the effect of some process parameters to determine the values of these parameters which give the best result, the distributions for the thickness and depths of the cup were used to estimate the effect of the parameters on the cup numerically, in addition to experimental verification just to the conditions which give the best numerical predictions in order to reduce the time, efforts and costs for producing square cup with less defects experimentally is the aim of this study. The numerical analysis is used to study

The successful implementation of deep learning nets opens up possibilities for various applications in viticulture, including disease detection, plant health monitoring, and grapevine variety identification. With the progressive advancements in the domain of deep learning, further advancements and refinements in the models and datasets can be expected, potentially leading to even more accurate and efficient classification systems for grapevine leaves and beyond. Overall, this research provides valuable insights into the potential of deep learning for agricultural applications and paves the way for future studies in this domain. This work employs a convolutional neural network (CNN)-based architecture to perform grapevine leaf image classifi

Conducted Althilelat chemical models of crude oil back to the reservoir Fertile from the fields of Baghdad and Kut and models of crude oil back to the reservoir ??????? of Haklbe Tikrit and Baghdad were calculated their properties Alvezaúah Kalkthaqh and weight, quality and degree of August j (API) and know the quality Nfothma that was light or heavy and make the comparison between Alinvtin also conducted chemical analyzes of the two models of Almia associated with each of the oil above Almkmnin and measured Ktvthma and Zojithma and concentrations of some dissolved salts in them and clarify the relationship between the oil reservoir and water associated with oil fields...

Identifying the total number of fruits on trees has long been of interest in agricultural crop estimation work. Yield prediction of fruits in practical environment is one of the hard and significant tasks to obtain better results in crop management system to achieve more productivity with regard to moderate cost. Utilized color vision in machine vision system to identify citrus fruits, and estimated yield information of the citrus grove in-real time. Fruit recognition algorithms based on color features to estimate the number of fruit. In the current research work, some low complexity and efficient image analysis approach was proposed to count yield fruits image in the natural scene. Semi automatic segmentation and yield calculation of fruit