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Bioremediation of Petroleum Polluted Soils using Consortium Bacteria
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      This study was carried out to isolate opportunistic hydrocarbons oil-degrading bacteria and develop a consortium or a mixture of bacteria with high biodegradation capabilities which can be used in biological treatment units of the contaminated water before release. The biological processes in general are environmentally friendly and cost effective, as they are easy to design and apply; as such they are more appropriate to the public.

    The location of the study was in Al-Dora refinery sludge holes area. The samples were collected for three seasons (winter, spring and summer) each consisted of three months.  The sludge samples were analyzed for various physical and chemical parameters. Temperature values of the sludge were at maximum in summer season, reaching 32ËšC, whereas they were at minimum in winter (24 ËšC). The values of sludge pH were at maximum in summer (9.70) and minimum in winter (9.20). Turbidity levels were 382 NTU in spring and 353 NUT in winter. Biological oxygen demand (BOD5) was at maximum in summer (760) and (690 mg/l) in winter. The maximum dissolved oxygen (DO) value of 5.20 mg/l was recorded in winter, while the minimum was 3.80 mg/l recorded in summer. The maximum electrical conductivity (EC) was 17130 μs/cm recorded in summer, while the minimum was 16150 μs/cm recorded in winter. The maximum total dissolved solids (TDS) values were 10335 mg/l recorded in summer, while the minimum (10015 mg/l) was recorded in winter. The maximum total petroleum hydrocarbon (TPH) value (431 mg/l) was recorded in summer, while the minimum (367 mg/l) was recorded in spring. Finally, the maximum salinity value (9.90%) was recorded in spring, while the minimum (9.30%) was recorded in winter. Also, hydrocarbon compounds in sludge samples were measured using Gas Chromatography - Mass Spectrometry (GC-MS), and the result showed that they were composed of 31 hydrocarbon compounds.In the present work, nineteen sludge degrading bacterial strains were isolated from the soil near Al-Dora refinery hole by primary and secondary screenings using a modified mineral salt medium supplemented with 1% (v/v) sludge as a carbon source. The most efficient two sludge degraded isolates identified by VITIK 2 compact were Kocuria rosea and Bacillus amyloliquefaciens. The tow isolates and there mixture showed best growth at 30°C for 12 days, as shown by the measurement of the optical density of the liquid culture and the final oil concentration by spectrophotometer.

     The bacterial isolates in liquid media with 2% (v/v) sludge showed best growth and the maximum biodegradation percentage after 12-day incubation period, as determined by gas chromatographic (GC). The degradation values were 68.9, 93.8 and 95.5% for Bacillus amyloliquefaciens, Kocuria rosea and the mixture of the tow isolates, respectively. In optimum conditions of pH 7, 40°C, 12 days incubation, the mixed bacterial consortium showed maximum sludge degradation.

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Publication Date
Wed Sep 01 2021
Journal Name
Baghdad Science Journal
Fuzzy-assignment Model by Using Linguistic Variables
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      This work addressed the assignment problem (AP) based on fuzzy costs, where the objective, in this study, is to minimize the cost. A triangular, or trapezoidal, fuzzy numbers were assigned for each fuzzy cost. In addition, the assignment models were applied on linguistic variables which were initially converted to quantitative fuzzy data by using the Yager’sorankingi method. The paper results have showed that the quantitative date have a considerable effect when considered in fuzzy-mathematic models.

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Publication Date
Sun Jan 30 2022
Journal Name
Iraqi Journal Of Science
Arabic Keywords Extraction using Conventional Neural Network
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    Keywords provide the reader with a summary of the contents of the document and play a significant role in information retrieval systems, especially in search engine optimization and bibliographic databases. Furthermore keywords help to classify the document into the related topic. Keywords extraction included manual extracting depends on the content of the document or article and the judgment of its author. Manual extracting of keywords is costly, consumes effort and time, and error probability. In this research an automatic Arabic keywords extraction model based on deep learning algorithms is proposed. The model consists of three main steps: preprocessing, feature extraction and classification to classify the document

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Publication Date
Wed Jan 13 2021
Journal Name
Iraqi Journal Of Science
YouTube Keyword Search Engine Using Speech Recognition
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Visual media is a better way to deliver the information than the old way of "reading". For that reason with the wide propagation of multimedia websites, there are large video library’s archives, which came to be a main resource for humans. This research puts its eyes on the existing development in applying classical phrase search methods to a linked vocal transcript and after that it retrieves the video, this an easier way to search any visual media. This system has been implemented using JSP and Java language for searching the speech in the videos

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Publication Date
Thu Oct 18 2018
Journal Name
Proceedings Of The Future Technologies Conference (ftc) 2018
Using Mouse Dynamics for Continuous User Authentication
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Publication Date
Tue Dec 07 2021
Journal Name
2021 14th International Conference On Developments In Esystems Engineering (dese)
Object Detection and Distance Measurement Using AI
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Publication Date
Wed Mar 10 2021
Journal Name
Baghdad Science Journal
Detecting Textual Propaganda Using Machine Learning Techniques
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Social Networking has dominated the whole world by providing a platform of information dissemination. Usually people share information without knowing its truthfulness. Nowadays Social Networks are used for gaining influence in many fields like in elections, advertisements etc. It is not surprising that social media has become a weapon for manipulating sentiments by spreading disinformation.  Propaganda is one of the systematic and deliberate attempts used for influencing people for the political, religious gains. In this research paper, efforts were made to classify Propagandist text from Non-Propagandist text using supervised machine learning algorithms. Data was collected from the news sources from July 2018-August 2018. After annota

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Publication Date
Wed Jul 14 2021
Journal Name
The Open Civil Engineering Journal
Producing Sustainable Concrete using Nano Recycled Glass
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Background:

Many tools and techniques have been recently adopted to develop construction materials that are less harmful and friendlier to the environment. New products can be achieved through the recycling of waste material. Thus, this study aims to use recycled glass bottles as sustainable materials.

Objective:

Our challenge is to use nano glass powder by the addition or replacement of the weight of the cement for producing concrete with enhanced strength.

Methods:

A nano recycled glass p

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Publication Date
Sat Jul 28 2018
Journal Name
Journal Of Engineering
Enhanced Oil Recovery using Smart Water Injection
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Smart water flooding (low salinity water flooding) was mainly invested in a sandstone reservoir. The main reasons for using low salinity water flooding are; to improve oil recovery and to give a support for the reservoir pressure.

In this study, two core plugs of sandstone were used with different permeability from south of Iraq to explain the effect of water injection with different ions concentration on the oil recovery. Water types that have been used are formation water, seawater, modified low salinity water, and deionized water.

The effects of water salinity, the flow rate of water injected, and the permeability of core plugs have been studied in order to summarize the best conditions of low salinity

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Publication Date
Sat Jan 01 2011
Journal Name
Journal Of Engineering
FILTRATION MODELING USING ARTIFICIAL NEURAL NETWORK (ANN)
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In this research Artificial Neural Network (ANN) technique was applied to study the filtration process in water treatment. Eight models have been developed and tested using data from a pilot filtration plant, working under different process design criteria; influent turbidity, bed depth, grain size, filtration rate and running time (length of the filtration run), recording effluent turbidity and head losses. The ANN models were constructed for the prediction of different performance criteria in the filtration process: effluent turbidity, head losses and running time. The results indicate that it is quite possible to use artificial neural networks in predicting effluent turbidity, head losses and running time in the filtration process, wi

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Publication Date
Sat Feb 09 2019
Journal Name
Journal Of The College Of Education For Women
Software Security using Aspect-Oriented Software Development
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Aspect-Oriented Software Development (AOSD) is a technology that helps achieving
better Separation of Concern (SOC) by providing mechanisms to identify all relevant points
in a program at which aspectual adaptations need to take place. This paper introduces a
banking application using of AOSD with security concern in information hiding.

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