The aim of the research is to apply fibrewise multi-emisssions of the paramount separation axioms of normally topology namely fibrewise multi-T0. spaces, fibrewise multi-T1 spaces, fibrewise multi-R0 spaces, fibrewise multi-Hausdorff spaces, fibrewise multi-functionally Hausdorff spaces, fibrewise multi-regular spaces, fibrewise multi-completely regular spaces, fibrewise multi-normal spaces and fibrewise multi-functionally normal spaces. Also we give many score regarding it.

We define and study new ideas of fibrewise topological space on D namely fibrewise multi-topological space on D. We also submit the relevance of fibrewise closed and open topological space on D. Also fibrewise multi-locally sliceable and fibrewise multi-locally section able multi-topological space on D. Furthermore, we propose and prove a number of statements about these ideas.

The objective of drilling parameters optimization in Majnoon oilfield is to arrive for a methodology that considers the past drilling data for five directional wells at 35 degree of inclination as a baseline for new wells to be drilled. Also, to predicts drilling performance by selecting the applied drilling parameters generated the highest rate of penetration (ROP) at each section. The focal point of the optimization process is to reduce drilling time and associated cost per each well. The results of this study show that the maximum ROP could not be achieved without sufficient flow rate to cool and clean the bit in clay intervals (36" and 24") hole sections. Although the influence of combination of Weight on Bit (WOB), Round per minute

The objective of drilling parameters optimization in Majnoon oilfield is to arrive for a methodology that considers the past drilling data for five directional wells at 35 degree of inclination as a baseline for new wells to be drilled. Also, to predicts drilling performance by selecting the applied drilling parameters generated the highest rate of penetration (ROP) at each section. The focal point of the optimization process is to reduce drilling time and associated cost per each well. The results of this study show that the maximum ROP could not be achieved without sufficient flow rate to cool and clean the bit in clay intervals (36" and 24") hole sections. Although the influence of combination of Weight on Bit (WOB), Round per mi

The CO2-Assisted Gravity Drainage process (GAGD) has been introduced to become one of the mostinfluential process to enhance oil recovery (EOR) methods in both secondary and tertiary recovery through immiscibleand miscible mode. Its advantages came from the ability of this process to provide gravity-stable oil displacement forenhancing oil recovery. Vertical injectors for CO2 gas have been placed at the crest of the pay zone to form a gas capwhich drain the oil towards the horizontal producing oil wells located above the oil-water-contact. The advantage ofhorizontal well is to provide big drainage area and small pressure drawdown due to the long penetration. Manysimulation and physical models of CO2-AGD process have been implemented

Among the undesirable effects of soil compaction is a measurable reduction in plant growth and crop yield. The prevailing belief is that compacted tillage pans are caused by repetitive farming practices, heavy tractors, tillage tools, and field traffic. This experiment was conducted to determine and map the hardpan layers across an agricultural field through advanced technologies of precision agriculture. These valuable techniques such as data logger, yield map, and data analysis of performance indicators were linked with accurate global positioning systems (GPS) datasets. These important technologies provided the farmers and helped them to identify and manage areas of the fields with higher compacted layers. Three ground speeds 4.3

Big data analysis is essential for modern applications in areas such as healthcare, assistive technology, intelligent transportation, environment and climate monitoring. Traditional algorithms in data mining and machine learning do not scale well with data size. Mining and learning from big data need time and memory efficient techniques, albeit the cost of possible loss in accuracy. We have developed a data aggregation structure to summarize data with large number of instances and data generated from multiple data sources. Data are aggregated at multiple resolutions and resolution provides a trade-off between efficiency and accuracy. The structure is built once, updated incrementally, and serves as a common data input for multiple mining an