Surface water flow samples were collected with distances downstream over Saqlawiya main drain whose stretch of about 24.5 km. The drain travels through different land use pattern, before, flowing into Tigris River. Eight sampling points were carefully
selected downstream the channel during dry season. The examined water parameters were pH, NH3, NO3, PO= 4, BOD5, COD, TDS, S.S, Cl-, SO= 4, Na+ , Ca+2, Mg+2, and Oil and Grease. Descriptive and inferential methods through finding the best curve fit correlation were employed in the study to test the strength of the association between water chemical characteristics and distance downstream the channel. A comparison of the values of chemical parameters at the Al-Saqlawiya Drain-Tigris River meeting shows that nine parameters (i.e. pH, NH3, NO3, PO= 4, BOD5, COD, TDS, S.S, and Oil and Grease) out of the 14 parameters under study decreased in
their concentration at meeting point. In a further analysis, predictive models were obtained through using six sampling points (0. 1, 3, 5,7, 8) and leaving three (2, 4, 6) as activation function. The percentage error for the calculated values of pH, NH3, BOD5, COD, TDS, Cl-.
SO= 4, Na+ and Mg+2 ranges from (0.54 to 15) % which demonstrate the high predictive capacity of the models. While the high values of the determination coefficient (r higher than 0.9) for NH3,NO3, BOD5, COD, SS, Cl-. SO= 4, Na+ , Mg+2 and Oil and grease, demonstrate a good model capacity relating water values and distances downstream Finally the paper concludes that artificial purification efforts at the water works should be directed towards controlling the concentration of Cl- , SO= 4, Na+ , Ca+2 , Mg+2 which increased with increases in distance downstream and suggests further research in the area of monitoring water quality.
The research aims to improve operational performance through the application of the Holonic Manufacturing System (HMS) in the rubber products factory in Najaf. The problem was diagnosed with the weakness of the manufacturing system in the factory to meet customers' demands on time within the available resources of machines and workers, which led to time delays of Processing and delivery, increased costs, and reduced flexibility in the factory, A case study methodology used to identify the reality of the manufacturing system and the actual operational performance in the factory. The simulation was used to represent the proposed (HMS) by using (Excel 2010) based on the actual data and calculate the operational performance measures
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Hospitals are part of the service organizations and most importantly at the level of individuals because they are tied to the people health and their daily lives , the nursing service is one of the important services provided by hospitals, and nurses are the human resource that offers this service, from this standpoint the idea of research came to prepare work Scheduling for nurses in a scientific way to improve performance operational for their services and provide efficient service available 24 hours a day, the research use one of the modern and scientific rules of scheduling its “schedule of
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Mindfulness is considered a process to draw an image of the active event and to creat new social varieties which leaves the individuals open to modernity and to be sensitive towards the context. in contrast, when individuals act with less attention, they need to be more determined concerning the varieties and events of the past . and as a result , they become unaware of the characteristics that creat the individual condition .The problem of the current study is represented in asking about the nature of the possible relationship between mindfulness and self-regulated learning within specific demographic frame of an importantsocial category represented in university students where no previous researches nor theories have agreed on the natu
... Show MoreThis paper proposes a self organizing fuzzy controller as an enhancement level of the fuzzy controller. The adjustment mechanism provides explicit adaptation to tune and update the position of the output membership functions of the fuzzy controller. Simulation results show that this controller is capable of controlling a non-linear time varying system so that the performance of the system improves so as to reach the desired state in a less number of samples.
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In this publication, several six coordinate bridged-polymeric metal complexes are reported. The reaction of 4,4`-dipyridine with ethyl chloroacetate in mole ratio of 1:2 gave the multidentate carboxylate ligand bis(N-carboxylatomethyl)-4,4`-dipyridinium). The reaction of the ligand with metal chloride and sodium azide resulted in the formation of the required polymeric complexes. Upon complex formation, the carboxylato ligand behaves as a neutral multidantate species. The mode of bonding and overall geometry of the complexes were determined through physicochemical and spectroscopic methods. These studies revealed octahedral geometry about metal centres and complexes of the general formula [Cr2(L)(N3)4]Cl2.H2O, Na2[Ag2(L)(N3)4].H2O and [M2(L
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The present investigation aimed to formulate a liquid self-microemulsifying drug delivery system (SMEDDS) of tacrolimus to enhance its oral bioavailability by improving its dispersibility and dissolution rate. Four liquid SMEDDS were prepared using maisine CC as oil phase, labrasol ALF as surfactant and transcutol HP as co-surfactant based on the solubility studies of tacrolimus in these components. The phase behavior of the components and the area of microemulsion were evaluated using pseudoternary phase diagrams. The formulations were also assessed for thermodynamic stability, robustness to dilution, self-emulsification time, drug content, globule size and polydispersity index. The prepared SMEDDS formulations exhibi
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Self-driving automobiles are prominent in science and technology, which affect social and economic development. Deep learning (DL) is the most common area of study in artificial intelligence (AI). In recent years, deep learning-based solutions have been presented in the field of self-driving cars and have achieved outstanding results. Different studies investigated a variety of significant technologies for autonomous vehicles, including car navigation systems, path planning, environmental perception, as well as car control. End-to-end learning control directly converts sensory data into control commands in autonomous driving. This research aims to identify the most accurate pre-trained Deep Neural Network (DNN) for predicting the steerin
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Transformers are a specific category of neural network design. Transformers often depend on extensive pre-training on a large scale and exhibit a notable degree of computational complexity. The disadvantage of using this method is a significant increase in computational complexity, which necessitates a significant commitment of time and computing resources in order to successfully work with these models. Transformer networks possess the desirable benefit of extracting distant characteristics effectively via their self-attention mechanism. In this paper, the Global Self-Attention Transformer module is applied to tackle these issues. The model is based on a segmentation problem called Brain-GS that works as a mechanism and encompasses
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Objectives: Assessment outcome of DOTS (Directly observed therapy short course) program in Al-Sader City
Sector that was established by the WHO.
Methodology: Three cohorts groups of patients attending Baghdad TB institute and TB center in Al-Sader city
were followed retrospectively. The 1st cohort included (314) patients registered in year (2003), the 2nd cohort
included (327) patients registered in year (2004), the 3rd cohort included (321) patients registered in year
(2005). The collected data were analyzed for case detection, treatment outcomes, retreatment outcomes,
treatment success, and retreatment success in regard to time, age and sex.
Results: The following rates were extracted for the three cohort: Case det