Tigris and Euphrates Rivers, originating in Turkey and cutting through both Syria and Iraq, have experienced drastic reductions in water flows in recent years due to increasing water demand, hydro-engineering projects, and climate change. The decline in water flows has led to decreased agricultural yields. Iraq reported its worst cereal harvest in a decade in 2009, indicating that with a decline in water supplies, a potential food security problem emerges. This study, therefore, addressed the effect of water availability on (1) Net Primary Productivity (NPP) and (2) Rain Use Efficiency (RUE) in the Tigris-Euphrates (T-E) basin. Results displayed that NPP increases significantly from the arid and semi-arid sites in the south to the wetter

Abstract The present work aims to study the performance of reinforced compacted clay soil by sand columns stabilized with sodium silicate to obtain more solid columns than the surrounding soil. The experimental work was carried out by using a lab model to evaluate the performance of both the floating and end bearing sand columns. The results showed that the improvement ratio for the soil reinforced with sand columns stabilized with sodium silicate reached 390% for the type of floating columns and 438% for end bearing columns.

Water pollution is an issue that can be exacerbated by drought as increased concentrations of unwanted substances are a consequence of lower water levels. Polluted water that flows into natural marshlands leads to the deposition of pollutants in the interior of the marsh. Here we present evidence that the interior of the Central Marsh (CM) in southern Iraq suffers from higher levels of pollution than areas closer to the source of water entering the marsh (the Euphrates River). A 1.7m embankment that halts the flow of the Euphrates is only infrequently breached and so the CM is effectively the terminal destination of the waters (and their associated pollutants and agricultural waste) flowing from the West of Iraq.

A range of water

The main aim of this study is to evaluate the remaining oil in previously produced zones, locate the water productive zone and look for any bypassed oil behind casing in not previously perforated intervals. Initial water saturation was calculated from digitized open hole logs using a cut-off value of 10% for irreducible water saturation. The integrated analysis of the thermal capture cross section, Sigma and Carbon/oxygen ratio was conducted and summarized under well shut-in and flowing conditions. The logging pass zone run through sandstone Zubair formation at north Rumaila oil field. The zones where both the Sigma and the C/O analysis show high remaining oil saturation similar to the open hole oil saturation, could be good oil zones that

The main aim of this study is to evaluate the remaining oil in previously produced zones, locate the water productive zone and look for any bypassed oil behind casing in not previously perforated intervals. Initial water saturation was calculated from digitized open hole logs using a cut-off value of 10% for irreducible water saturation. The integrated analysis of the thermal capture cross section, Sigma and Carbon/oxygen ratio was conducted and summarized under well shut-in and flowing conditions. The logging pass zone run through sandstone Zubair formation at north Rumaila oil field. The zones where both the Sigma and the C/O analysis show high remaining oil saturation simila

Diabetes is one of the increasing chronic diseases, affecting millions of people around the earth. Diabetes diagnosis, its prediction, proper cure, and management are compulsory. Machine learning-based prediction techniques for diabetes data analysis can help in the early detection and prediction of the disease and its consequences such as hypo/hyperglycemia. In this paper, we explored the diabetes dataset collected from the medical records of one thousand Iraqi patients. We applied three classifiers, the multilayer perceptron, the KNN and the Random Forest. We involved two experiments: the first experiment used all 12 features of the dataset. The Random Forest outperforms others with 98.8% accuracy. The second experiment used only five att

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

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