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Stresses and pore water pressure induced by machine foundation on saturated sand
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In this study, the response and behavior of machine foundations resting on dry and saturated sand was investigated experimentally. In order to investigate the response of soil and footing to steady state dynamic loading, a physical model was manufactured. The manufactured physical model could be used to simulate steady state harmonic load at different operating frequencies. Total of (84) physical models were performed. The parameters that were taken into considerations include loading frequency, size of footing and different soil conditions. The footing parameters were related to the size of the rectangular footing and depth of embedment. Two sizes of rectangular steel model footing were used (100 200 12.5 mm) and (200 400 5.0 mm). The footing was tested in all parameters at the surface and at 50 mm depth below model surface. Meanwhile the investigated parameters of the soil condition included dry and saturated sand for two relative densities 30% and 80%. The response of the soil to dynamic loading includes measuring the stresses inside the soil using piezoelectric sensors as well as measuring the excess pore water pressure by using pore water pressure transducers. It was found that the rate of increase in excess pore water pressure ratio decreased remarkably at a depth of 0.5 B–1.5 B (B is the footing width) for medium and loose dense sand, respectively. Moreover, excess pore water pressure ratio increases with increasing the eccentricity of dynamic load. The generated pore water pressure is always greater under the point of load application. Its value reduces with a certain percentages at any point away from the point of load application. In addition, the rate of variation of pore water pressure with eccentricity for loose sand is less than that for medium dense sand. The dynamic stress increments resulting from the dynamic load on the foundation reduce with depth. In addition, the dynamic stresses under the corner are slightly greater than the stresses at the center by a percentage of about 10.0%. The excess pore water pressure increases with increasing the relative density of the sand, the amplitude of dynamic loading and the operating frequency. In contrast, the rate of dissipation of the excess pore water pressure during dynamic loading is more in the case of loose sand.

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Publication Date
Mon Oct 05 2015
Journal Name
Diyala Journal For Pure Science
Determination of zinc ions in drinking water distribution systems (PVC pipes) by spectrophotometric method
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In this work, a method for the simultaneous spectrophotometric determination of zinc which was precipitated into deionized water that is in a commercial distribution systems PVC pipe, is proposed using UV-VIS Spectrophotometer. The method based on the reaction between the analytes Zn2+ and 2-carboxy-2-hyroxy-5-sulfoformazylbenze (Zincon) at an absorption maximum of 620nm at pH 9-10. This ligand is selective reagent. Since the complex is colored (blue), its stoichiometry can be established using visible spectrometry to measure the absorbance of solutions of known composition. The stoichiometry of the complex was determined by Job’s method and molar ratio method and found to be 1:2 (M: L). A series of synthetic solution containing different

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Publication Date
Wed Jun 01 2022
Journal Name
Journal Of Water Process Engineering
Antibiotics adsorption from contaminated water by composites of ZSM-5 zeolite nanocrystals coated carbon
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The presence of antibiotic residues such as ciprofloxacin (CIPR) in an aqueous environment is dangerous when their concentrations exceed the allowable. Therefore, eliminating these residues from the wastewater becomes an essential issue to prevent their harm. In this work, the potential of efficient adsorption of ciprofloxacin antibiotics was studied using eco-friendly ZSM-5 nanocrystals‑carbon composite (NZC). An inexpensive effective natural binder made of the sucrose-citric acid mixture was used for preparing NZC. The characterization methods revealed the successful preparation of NZC with a favorable surface area of 103.739 m2/g, and unique morphology and functional groups. Investigating the ability of NZC for adsorbing CIPR antibioti

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Publication Date
Sun Mar 13 2011
Journal Name
Baghdad Science Journal
The Mutagenic Effect of water Extracts of Malva parviflora by Bacterial System (part II )
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This study was carried out in order to determine the toxic, mutagenic and antimutagenic effects for Mallow (Malva parviflora) in comparison to its mutagenic effect of Ultraviolet (UV) because it is consider physical mutagen by using parameters for the extract pri , with , post UV exposure by using bacterial system (G-system). The used system consisted of three isolates G3 Bacillus spp., G12 Arthrobacter spp. and G27 Brevibacterium spp.. The study depended on recording survival fraction (Sx) for studying the effects and induction of Streptomycin and Refampicin resistance mutants as a genetic markers.Water Extract was prepared from fresh and dry mallow leaves, stems, flowers and roots, in optimum concentration equal to (125µg/ml) which is

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Publication Date
Wed Mar 01 2017
Journal Name
Diyala Journal Of Engineering Sciences
NFLUENCE OF WATER SOURCE ON COMPRESSIVE STRENGTH OF HIGH STRENGTH CONCRETE
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This research studies the influence of water source on the compressive strength of high strength concrete. Four types of water source were adopted in both mixing and curing process these are river, tap, well and drainage water (all from Iraq-Diyala governorate). Chemical analysis was carried out for all types of the used water including (pH, total dissolved solids (TDS), Turbidity, chloride, total suspended solid (TSS), and sulfates). Depending on the chemical analysis results, it was found that for all adopted sources the chemical compositions was within the ASTM C 1602/C 1602M-04 limits and can be satisfactorily used in concrete mixtures. Mixture of high strength concrete for compressive strength of (60 MPa) was designed and checked using

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Publication Date
Fri Jul 19 2024
Journal Name
Baghdad Science Journal
An Analytical Comparison of the Behavior of Machine Learning and Deep Learning in Stock Market Prediction
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Machine learning is considered a powerful technique in many applications such as classification, clustering, recognition and prediction. Deep learning is a modern, vital and superior machine learning that gives stunning performance, especially with huge data. Stock market price prediction is the process of determining the future value of a prospect of a financial instrument traded in the market, to gain a great profit a successful prediction must be conducted, in order to achieve that machine learning is used, in this article, two approaches are proposed to predict the stock market prices and movement using two datasets, the first approach employs two machine learning models (J48 & logistic regression) while the second approach based on rec

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Publication Date
Sat Jan 01 2022
Journal Name
Proceedings Of International Conference On Computing And Communication Networks
Automatic Health Speech Prediction System Using Support Vector Machine
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Publication Date
Mon Jun 30 2025
Journal Name
Ingénierie Des Systèmes D Information
Comparative Analysis of Four Programming Languages for Machine Learning
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Publication Date
Mon Mar 14 2022
Journal Name
Periodicals Of Engineering And Natural Sciences (pen)
Mathematical simulation of memristive for classification in machine learning
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Publication Date
Thu Sep 01 2022
Journal Name
Iraqi Journal Of Computers, Communications, Control And Systems Engineering
A Framework for Predicting Airfare Prices Using Machine Learning
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Many academics have concentrated on applying machine learning to retrieve information from databases to enable researchers to perform better. A difficult issue in prediction models is the selection of practical strategies that yield satisfactory forecast accuracy. Traditional software testing techniques have been extended to testing machine learning systems; however, they are insufficient for the latter because of the diversity of problems that machine learning systems create. Hence, the proposed methodologies were used to predict flight prices. A variety of artificial intelligence algorithms are used to attain the required, such as Bayesian modeling techniques such as Stochastic Gradient Descent (SGD), Adaptive boosting (ADA), Decision Tre

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Publication Date
Sat Jan 01 2022
Journal Name
Journal Of Cybersecurity And Information Management
Machine Learning-based Information Security Model for Botnet Detection
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Botnet detection develops a challenging problem in numerous fields such as order, cybersecurity, law, finance, healthcare, and so on. The botnet signifies the group of co-operated Internet connected devices controlled by cyber criminals for starting co-ordinated attacks and applying various malicious events. While the botnet is seamlessly dynamic with developing counter-measures projected by both network and host-based detection techniques, the convention techniques are failed to attain sufficient safety to botnet threats. Thus, machine learning approaches are established for detecting and classifying botnets for cybersecurity. This article presents a novel dragonfly algorithm with multi-class support vector machines enabled botnet

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