The Iraqi marshes are considered the most extensive wetland ecosystem in the Middle East and are located in the middle and lower basin of the Tigris and Euphrates Rivers which create a wetlands network and comprise some shallow freshwater lakes that seasonally swamped floodplains. Al-Hawizeh marsh is a major marsh located east of Tigris River south of Iraq. This study aims to assess water quality through water quality index (WQI) and predict Total Dissolved Solids (TDS) concentrations in Al-Hawizeh marsh based on artificial neural network (ANN). Results showed that the WQI was more than 300 for years 2013 and 2014 (Water is unsuitable for drinking) and decreased within the range 200-300 in years 2015 and 2016 (Very poor water). The developed ANN mode gave a high correlation coefficient reaching 0.927 for the prediction of TDS from the model and showed high levels of TDS in Al-Hawizeh marsh that pose threats to people using the marsh for drinking and other uses. The dissolved Oxygen concentration has the highest importance of 100% in the model because the water of the marsh is fresh water, while Turbidity had the lowest importance.
The prevalence of using the applications for the internet of things (IoT) in many human life fields such as economy, social life, and healthcare made IoT devices targets for many cyber-attacks. Besides, the resource limitation of IoT devices such as tiny battery power, small storage capacity, and low calculation speed made its security a big challenge for the researchers. Therefore, in this study, a new technique is proposed called intrusion detection system based on spike neural network and decision tree (IDS-SNNDT). In this method, the DT is used to select the optimal samples that will be hired as input to the SNN, while SNN utilized the non-leaky integrate neurons fire (NLIF) model in order to reduce latency and minimize devices
... Show MoreThe prevalence of using the applications for the internet of things (IoT) in many human life fields such as economy, social life, and healthcare made IoT devices targets for many cyber-attacks. Besides, the resource limitation of IoT devices such as tiny battery power, small storage capacity, and low calculation speed made its security a big challenge for the researchers. Therefore, in this study, a new technique is proposed called intrusion detection system based on spike neural network and decision tree (IDS-SNNDT). In this method, the DT is used to select the optimal samples that will be hired as input to the SNN, while SNN utilized the non-leaky integrate neurons fire (NLIF) model in order to reduce latency and minimize devices
... Show MoreThis study was conducted to estimate the extent of damage to the population in Basra, southern Iraq, specifically the areas adjacent to the Shatt al-Arab and the Arabian Gulf, which are the Al-Fao district and the Al-Siba region. They are affected by the progression of saline water resulting from the lack of water imports and the Karun River interruption, which led to high concentrations of salts in the Shatt Al-Arabs. Consequently, its effect on lands and all life types in these areas requires correcting a map of the study area to drop the groundwater sites as well as calculate the total dissolved salts, electrical conductivity and pH. This study concluded that the groundwater contains very high percentages of total dissolved solid
... Show MoreThe research aims to measure the economic efficiency and technological change and the total productivity of resources using the parameter and non-parameter methods, for agricultural companies registered in the Iraqi stock exchange, the number of 6 companies for the period from 2005 to 2017 based on the hypothesis that the agricultural companies do not achieve economic efficiency and does not control the management of its operations, and It may be technically efficient but the size of its operations is not optimal. From non-parametric methods, the data envelope analysis method was used. Using the DEAP program, the Middle East Company achieved the highest average technical and cost efficiency of 0.62 and 0.58, respectively. The Iraq
... Show MoreDue to the deliberate disposal of industrial waste, a great amount of petroleum hydrocarbons pollute the soil and aquatic environments. Bioremediation that depends on the microorganisms in the removal of pollutants is more efficient and cost-effective technology. In this study, five rhizobacteria were isolated from Phragmites australis roots and exposed to real wastewater from Al-Daura refinery with 70 mg/L total petroleum hydrocarbons (TPH) concentration. The five selected rhizobacteria were examined in a biodegradation test for seven days to remove TPH. The results showed that 80% TPH degradation as the maximum value by Sphingomonas Paucimobilis as identified with Vitek® 2 Compact (France).
Three stations were chosen on the water treatment plan of al- madaan .The Samples collected from the (Raw water) and the Sedimentation, filtration and storage water and the drinking water of outlet. Coliform densities T.S and F.C and TS and F.S and total bacterial count as bacteriological pollution indicators, as moste probable number (MPN) method was studied in test. Also some of the chemical characteristics of the water like pH , total suspended solid T.S.S, T.D.D.and S04 , T.Hardness , Ca++ , Mg++ . From the results it were indicated . The study showed the drinking water of outlet (distriputed in system) was agree with WHO criteria and Iraqi limits standards .
Abstract
The study aims to identify the common fears of preschool children and their relationship to the approaches to parental treatment in South Al Batinah Governorate from their mother’s point of view. Total of (466) mothers were selected as the study sample. The researcher used the scale of common fear and the scale of parental treatment approaches. The results of the study have shown that the most common fear among the study sample was (the fear of darkness) in the first level with a rate of 75.03%, and in the second level came the item (my child is afraid to sleep alone) by 72.74%, in the third level came to the item (fear of seeing insects) with a rate of 67.59%, and the last one was (the fear of rain) w
... Show MoreSoils at Al-Koot-Btera were choosen to determine their sedimentary
environments. It is found that there are , five soil series and as mentioned :
MF11-MW9-DM97-DM57-DF95 . The five found soil series are of internal
well drained ,moderate and imperfect. Their textures vary in moderately,fine
and moderately fine.
indicating that sediments rang from poorly to very poorly sorting. Values of median
diameter Md Ø range between 4.11-7.80 Ø .The relation between the sorting
and median diameter shows that 95.24% of samples is a sedimentary
environment of aquite river , while 4.76% is aeolian sediments.
The values of meso to platy kurtic of most horizon materials rang
between 0.67-1.26 Ø .That is to say the samples
The paper present design of a control structure that enables integration of a Kinematic neural controller for trajectory tracking of a nonholonomic differential two wheeled mobile robot, then proposes a Kinematic neural controller to direct a National Instrument mobile robot (NI Mobile Robot). The controller is to make the actual velocity of the wheeled mobile robot close the required velocity by guarantees that the trajectory tracking mean squire error converges at minimum tracking error. The proposed tracking control system consists of two layers; The first layer is a multi-layer perceptron neural network system that controls the mobile robot to track the required path , The second layer is an optimization layer ,which is impleme
... Show MoreAerial Robot Arms (ARAs) enable aerial drones to interact and influence objects in various environments. Traditional ARA controllers need the availability of a high-precision model to avoid high control chattering. Furthermore, in practical applications of aerial object manipulation, the payloads that ARAs can handle vary, depending on the nature of the task. The high uncertainties due to modeling errors and an unknown payload are inversely proportional to the stability of ARAs. To address the issue of stability, a new adaptive robust controller, based on the Radial Basis Function (RBF) neural network, is proposed. A three-tier approach is also followed. Firstly, a detailed new model for the ARA is derived using the Lagrange–d’A
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