The aim of this paper is to design artificial neural network as an alternative accurate tool to estimate concentration of Cadmium in contaminated soils for any depth and time. First, fifty soil samples were harvested from a phytoremediated contaminated site located in Qanat Aljaeesh in Baghdad city in Iraq. Second, a series of measurements were performed on the soil samples. The inputs are the soil depth, the time, and the soil parameters but the output is the concentration of Cu in the soil for depth x and time t. Third, design an ANN and its performance was evaluated using a test data set and then applied to estimate the concentration of Cadmium. The performance of the ANN technique was compared with the traditional laboratory inspecting using the training and test data sets. The results of this work show that the ANN technique trained on experimental measurements can be successfully applied to the rapid estimation of Cadmium concentration
Estimation of mechanical and physical rock properties is an essential issue in applications related to reservoir geomechanics. Carbonate rocks have complex depositional environments and digenetic processes which alter the rock mechanical properties to varying degrees even at a small distance. This study has been conducted on seventeen core plug samples that have been taken from different formations of carbonate reservoirs in the Fauqi oil field (Jeribe, Khasib, and Mishrif formations). While the rock mechanical and petrophysical properties have been measured in the laboratory including the unconfined compressive strength, Young's modulus, bulk density, porosity, compressional and shear -waves, well logs have been used to do a compar
... Show MoreThe objective of this research was to estimate the dose distribution delivered by radioactive gold nanoparticles (198 AuNPs or 199 AuNPs) to the tumor inside the human prostate as well as to normal tissues surrounding the tumor using the Monte-Carlo N-Particle code (MCNP-6.1. 1 code). Background Radioactive gold nanoparticles are emerging as promising agents for cancer therapy and are being investigated to treat prostate cancer in animals. In order to use them as a new therapeutic modality to treat human prostate cancer, accurate radiation dosimetry simulations are required to estimate the energy deposition in the tumor and surrounding tissue and to establish the course of therapy for the patient. Materials and methods A simple geometrical
... Show MoreCommunication represents the essence of language learning. Since the unspecified evolution of conveying information, human beings have been employing the main constituents of language with short pauses. Although the punctuation marks necessitate short expressions among thought group of words in writing, human language demand for understanding how and when to pause orally. This paper presents the pause technique in the classroom. It signifies the relation between pausing and lecturing in the class and determines its sufficient time-management to interact with college learners of different specializations. The conduct study reviewed teaching pause technique in the empirical studies at Special Education and Communication Disorders of Pennsylva
... Show MoreThe map of permeability distribution in the reservoirs is considered one of the most essential steps of the geologic model building due to its governing the fluid flow through the reservoir which makes it the most influential parameter on the history matching than other parameters. For that, it is the most petrophysical properties that are tuned during the history matching. Unfortunately, the prediction of the relationship between static petrophysics (porosity) and dynamic petrophysics (permeability) from conventional wells logs has a sophisticated problem to solve by conventional statistical methods for heterogeneous formations. For that, this paper examines the ability and performance of the artificial intelligence method in perme
... Show MoreImitation learning is an effective method for training an autonomous agent to accomplish a task by imitating expert behaviors in their demonstrations. However, traditional imitation learning methods require a large number of expert demonstrations in order to learn a complex behavior. Such a disadvantage has limited the potential of imitation learning in complex tasks where the expert demonstrations are not sufficient. In order to address the problem, we propose a Generative Adversarial Network-based model which is designed to learn optimal policies using only a single demonstration. The proposed model is evaluated on two simulated tasks in comparison with other methods. The results show that our proposed model is capable of completing co
... Show MoreEnergy savings are very common in IoT sensor networks because IoT sensor nodes operate with their own limited battery. The data transmission in the IoT sensor nodes is very costly and consume much of the energy while the energy usage for data processing is considerably lower. There are several energy-saving strategies and principles, mainly dedicated to reducing the transmission of data. Therefore, with minimizing data transfers in IoT sensor networks, can conserve a considerable amount of energy. In this research, a Compression-Based Data Reduction (CBDR) technique was suggested which works in the level of IoT sensor nodes. The CBDR includes two stages of compression, a lossy SAX Quantization stage which reduces the dynamic range of the
... Show MoreDiscriminant between groups is one of the common procedures because of its ability to analyze many practical phenomena, and there are several methods can be used for this purpose, such as linear and quadratic discriminant functions. recently, neural networks is used as a tool to distinguish between groups.
In this paper the simulation is used to compare neural networks and classical method for classify observations to group that is belong to, in case of some variables that don’t follow the normal distribution. we use the proportion of number of misclassification observations to the all observations as a criterion of comparison.
The central marshes are one of the most important wetlands/ecosystems in the southern area of Iraq. This study evaluates the bed soil's mechanical, physical, and chemical properties at certain southern Iraqi central marshes sites. This was conducted to investigate their types and suitability for enhancing the agricultural reality of most field crops and for construction purposes. Soil samples were collected from 15 sites at 10-100 cm depth. Hence, numerous parameters were determined: index properties, unconfined compressive strength, direct shear strength, consolidation, texture, and sieve analysis, water content, specific gravity, dry density, permeability, pH, total soluble salts (TSS), organic materials (OM) and total
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